### Standard imports
import pandas as pd
import numpy as np
import os, sys, site, pickle, json, shapely, zipfile
import matplotlib.pyplot as plt
import matplotlib as mpl
from glob import glob
from tqdm import tqdm, trange, tqdm_notebook, tnrange
import urllib, requests
import geopandas as gpd
import pvlib
from IPython.display import display, HTML
from matplotlib.ticker import (
AutoMinorLocator, MultipleLocator, AutoLocator, PercentFormatter)
pd.options.display.max_columns = 200
pd.options.display.max_rows = 15
%config InlineBackend.figure_format = 'retina'
### Import the package
### (Note: scriptpath should lead to folder above 'pvvm' folder)
# scriptpath = os.path.expanduser('~/path/to/folder/above/pvvm/')
scriptpath = os.path.expanduser('~/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/')
site.addsitedir(scriptpath)
import pvvm
revmpath = pvvm.settings.revmpath
datapath = pvvm.settings.datapath
pvvm.plots.plotparams()
### Load TMY-optimized orientations
dfin = pd.read_csv(revmpath+'out/results_pvtos_tmyopt_public.csv.gz')
for col in ['ISOwecc','ISO:Node']:
dfin[col] = dfin[col].astype('category')
dfin['yearsun'] = dfin['yearsun'].map(lambda x: 'tmy' if x == 'tmy' else int(float(x))).copy()
### Drop the Canada nodes
dfin = dfin.loc[~(dfin['ISO:Node'].isin(['MISO:MHEB','MISO:SPC']))].reset_index(drop=True)
### Load final revenue and CF results calculated under historical irradiance
dfin_optorient_histsun = pd.read_csv(revmpath+'out/results_pvtos_histsun_public.csv.gz')
for col in ['ISOwecc','ISO:Node']:
dfin_optorient_histsun[col] = dfin_optorient_histsun[col].astype('category')
dfin_optorient_histsun['yearsun'] = dfin_optorient_histsun['yearsun'].map(
lambda x: 'tmy' if x == 'tmy' else int(float(x))).copy()
### Drop the Canada nodes
dfin_optorient_histsun = dfin_optorient_histsun.loc[
~(dfin_optorient_histsun['ISO:Node'].isin(['MISO:MHEB','MISO:SPC']))].reset_index(drop=True)
/Users/patrickbrown/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (12,38,39,41,45,46,51,55,56,57,61) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result) /Users/patrickbrown/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (2,3,6,31) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result)
isos = ['CAISO','ERCOT','MISO','PJM','NYISO','ISONE']
colors = dict(zip(isos, ['C{}'.format(i) for i in range(len(isos))]))
alpha = 0.2
years = list(range(2010,2018))
mc = {'tmy':'k','da':'C0', 'rt':'C3'}
bootstrap = 5 ### Use 5 for testing and 5000 for publication
figheight = 12
innerband = ['25%','75%']
outerband = ['2.5%','97.5%']
percentiles = innerband + ['50%'] + outerband
fractions = [float(pct[:pct.find('%')])*.01 for pct in percentiles]
labels = {
'outer': 'central {:.0f}%'.format((fractions[-1]-fractions[-2])*100),
'inner': 'central {:.0f}%'.format((fractions[1]-fractions[0])*100),
'median': 'median',
'min': 'min & max',
'max': '_nolabel_',
'minmax': 'all nodes',
'minmax': 'min & max',
}
################# All together
######### Assemble plot data
mergecols = ['ISOwecc','ISO:Node','yearlmp']
###### Fixed DA: optrev_rev vs optcf_rev; orientations
program = 'PVvalueOptV4'
systemtype = 'fixed'
yearsun = 'tmy'
###### Must-run
pricecutoff = 'no'
dfplot = dfin.loc[
(dfin.market == 'da')
& (dfin.yearlmp.isin(years))
& (dfin.program == program)
& (dfin.systemtype == systemtype)
& (dfin.yearsun == yearsun)
& (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Rev/OptCF_Rev','OptRev_Azimuth','OptRev_Tilt',
'OptRev_CF/OptCF_CF', 'OptCF_Azimuth','OptCF_Tilt',
'OptCF_Rev','OptRev_Rev',
]].copy()
dfrt = dfin.loc[
(dfin.market == 'rt')
& (dfin.yearlmp.isin(years))
& (dfin.program == program)
& (dfin.systemtype == systemtype)
& (dfin.yearsun == yearsun)
& (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Rev/OptCF_Rev','OptRev_Azimuth','OptRev_Tilt',
'OptRev_CF/OptCF_CF', 'OptCF_Azimuth','OptCF_Tilt',
'OptCF_Rev','OptRev_Rev',
]].copy()
dfplot = dfplot.merge(dfrt, on=mergecols, suffixes=('(da)','(rt)'), how='outer')
###### Curtailable
pricecutoff = '0'
dfcurtail = dfin.loc[
(dfin.market == 'da')
& (dfin.yearlmp.isin(years))
& (dfin.program == program)
& (dfin.systemtype == systemtype)
& (dfin.yearsun == yearsun)
& (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Azimuth','OptRev_Tilt',
'OptCF_Azimuth','OptCF_Tilt',
'OptCF_Rev','OptRev_Rev',
]].copy()
dfrt = dfin.loc[
(dfin.market == 'rt')
& (dfin.yearlmp.isin(years))
& (dfin.program == program)
& (dfin.systemtype == systemtype)
& (dfin.yearsun == yearsun)
& (dfin.pricecutoff == pricecutoff)
][mergecols+['OptRev_Azimuth','OptRev_Tilt',
'OptCF_Azimuth','OptCF_Tilt',
'OptCF_Rev','OptRev_Rev',
]].copy()
dfcurtail = dfcurtail.merge(dfrt, on=mergecols, suffixes=('(da)','(rt)'))
dfplot = dfplot.merge(dfcurtail, on=mergecols, suffixes=('(mustrun)','(curtail)'))
###### Track, DA and RT: Rev_curtail/Rev_mustrun
program = 'PVvalueV8'
pricecutoff = '0'
systemtype = 'track'
##########
### Ratios
datum = 'Rev_curtail/Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
dfmerge = dfin.loc[
(dfin.market == market)
& (dfin.yearlmp.isin(years))
& (dfin.program == program)
& (dfin.systemtype == systemtype)
& (dfin.yearsun == dfin.yearlmp)
& (dfin.Product == 'lmp')
].copy()
### Get ratio
dfmerge[datum+'({})'.format(market)] = (
dfmerge['Revenue_dispatched'] / dfmerge['Revenue'])
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[datum+'({})'.format(market)]],
on=mergecols, how='outer')
datum = 'CF_curtail/CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
dfmerge = dfin.loc[
(dfin.market == market)
& (dfin.yearlmp.isin(years))
& (dfin.program == program)
& (dfin.systemtype == systemtype)
& (dfin.yearsun == dfin.yearlmp)
& (dfin.Product == 'lmp')
].copy()
### Get ratio
dfmerge[datum+'({})'.format(market)] = (
dfmerge['CapacityFactor_dispatched'] / dfmerge['CapacityFactor'])
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[datum+'({})'.format(market)]],
on=mergecols, how='outer')
###############
### Differences
datum = 'Rev_curtail-Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
dfmerge = dfin.loc[
(dfin.market == market)
& (dfin.yearlmp.isin(years))
& (dfin.program == program)
& (dfin.systemtype == systemtype)
& (dfin.yearsun == dfin.yearlmp)
& (dfin.Product == 'lmp')
].copy()
### Get ratio
dfmerge[datum+'({})'.format(market)] = (
dfmerge['Revenue_dispatched'] - dfmerge['Revenue'])
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[datum+'({})'.format(market)]],
on=mergecols, how='outer')
datum = 'CF_curtail-CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
dfmerge = dfin.loc[
(dfin.market == market)
& (dfin.yearlmp.isin(years))
& (dfin.program == program)
& (dfin.systemtype == systemtype)
& (dfin.yearsun == dfin.yearlmp)
& (dfin.Product == 'lmp')
].copy()
### Get ratio
dfmerge[datum+'({})'.format(market)] = (
dfmerge['CapacityFactor_dispatched'] - dfmerge['CapacityFactor'])
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[datum+'({})'.format(market)]],
on=mergecols, how='outer')
# dfplot = dfplot.dropna(thresh=10).copy()
###############################
######### Historical irradiance
data = ['Revenue','CapacityFactor','ValueAverage','ValueFactor',
'Revenue_dispatched','CapacityFactor_dispatched',
'ValueAverage_dispatched','ValueFactor_dispatched',]
dicthist = {}
for market in ['da','rt']:
############ Revenue and CF ratios, fixed-tilt
###### Must-run
df = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.systemtype == 'fixed')
& (dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.mod0 == 'optcf')
& (dfin_optorient_histsun.mod1 == '0'),
['ISO:Node','yearlmp']+data,
].sort_values(['yearlmp','ISO:Node'])
df.index = df[['ISO:Node','yearlmp']]
for datum in ['Revenue','CapacityFactor','ValueAverage','ValueFactor']:
dicthist['{}_hist_optcf_f({})(mustrun)'.format(datum,market)] = df[datum]
df = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.systemtype == 'fixed')
& (dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.mod0 == 'optrev')
& (dfin_optorient_histsun.mod1 == '0'),
['ISO:Node','yearlmp']+data,
].sort_values(['yearlmp','ISO:Node'])
df.index = df[['ISO:Node','yearlmp']]
for datum in ['Revenue','CapacityFactor','ValueAverage','ValueFactor']:
dicthist['{}_hist_optrev_f({})(mustrun)'.format(datum,market)] = df[datum]
###### Curtailable
df = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.systemtype == 'fixed')
& (dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.mod0 == 'optcf')
& (dfin_optorient_histsun.mod1 == 'opt0cutoff'),
['ISO:Node','yearlmp']+data,
].sort_values(['yearlmp','ISO:Node'])
df.index = df[['ISO:Node','yearlmp']]
for datum in ['Revenue_dispatched','CapacityFactor_dispatched',
'ValueAverage_dispatched','ValueFactor_dispatched']:
dicthist['{}_hist_optcf_f({})(curtail)'.format(datum,market)] = df[datum]
df = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.systemtype == 'fixed')
& (dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.mod0 == 'optrev')
& (dfin_optorient_histsun.mod1 == 'opt0cutoff'),
['ISO:Node','yearlmp']+data,
].sort_values(['yearlmp','ISO:Node'])
df.index = df[['ISO:Node','yearlmp']]
for datum in ['Revenue_dispatched','CapacityFactor_dispatched',
'ValueAverage_dispatched','ValueFactor_dispatched']:
dicthist['{}_hist_optrev_f({})(curtail)'.format(datum,market)] = df[datum]
############ Tracking vs fixed CFopt, DA, must-run
######### must-run
for market in ['da', 'rt']:
dftop = dfin.loc[
(dfin.market == market)
& (dfin.yearlmp.isin(years))
& (dfin.program == 'PVvalueV8')
& (dfin.systemtype == 'track')
& (dfin.yearsun == dfin.yearlmp)
& (dfin.Product == 'lmp')
].sort_values(['yearlmp','ISO:Node']).copy()
dftop.index = dftop[['ISO:Node','yearlmp']]
dfbot = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.systemtype == 'fixed')
& (dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.mod0 == 'optcf')#,
& (dfin_optorient_histsun.mod1 == '0')
# ['ISO:Node','yearlmp','Revenue','CapacityFactor']
].sort_values(['yearlmp','ISO:Node']).copy()
dfbot.index = dfbot[['ISO:Node','yearlmp']]
if len(dftop) != len(dfbot):
print(market)
print('len(dftop) = {}'.format(len(dftop)))
print('len(dfbot) = {}'.format(len(dfbot)))
raise Exception('unequal lengths')
dicthist['Revenue_hist_default_t({})(mustrun)'.format(market)] = dftop['Revenue']
dicthist['Revenue_track(def)/fixed(optcf)_hist,{},mustrun'.format(market)] = (
dftop['Revenue'] / dfbot['Revenue'])
dicthist['CapacityFactor_hist_default_t({})(mustrun)'.format(market)] = dftop['CapacityFactor']
dicthist['CapacityFactor_track(def)/fixed(optcf)_hist,{},mustrun'.format(market)] = (
dftop['CapacityFactor'] / dfbot['CapacityFactor'])
dicthist['Value_hist_default_t({})(mustrun)'.format(market)] = dftop['ValueAverage']
dicthist['Value_track(def)/fixed(optcf)_hist,{},mustrun'.format(market)] = (
dftop['ValueAverage'] / dfbot['ValueAverage'])
dicthist['ValueFactor_hist_default_t({})(mustrun)'.format(market)] = dftop['ValueFactor']
###############
### Differences
dicthist['Revenue_track(def)-fixed(optcf)_hist,{},mustrun'.format(market)] = (
dftop['Revenue'] - dfbot['Revenue'])
dicthist['CapacityFactor_track(def)-fixed(optcf)_hist,{},mustrun'.format(market)] = (
dftop['CapacityFactor'] - dfbot['CapacityFactor'])
dicthist['Value_track(def)-fixed(optcf)_hist,{},mustrun'.format(market)] = (
dftop['ValueAverage'] - dfbot['ValueAverage'])
######### curtailable
for market in ['da', 'rt']:
dftop = dfin.loc[
(dfin.market == market)
& (dfin.yearlmp.isin(years))
& (dfin.program == 'PVvalueV8')
& (dfin.systemtype == 'track')
& (dfin.yearsun == dfin.yearlmp)
& (dfin.Product == 'lmp')
].sort_values(['yearlmp','ISO:Node']).copy()
dftop.index = dftop[['ISO:Node','yearlmp']]
dfbot = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.systemtype == 'fixed')
& (dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.mod0 == 'optcf')#,
& (dfin_optorient_histsun.mod1 == 'opt0cutoff')
# ['ISO:Node','yearlmp','Revenue','CapacityFactor']
].sort_values(['yearlmp','ISO:Node']).copy()
dfbot.index = dfbot[['ISO:Node','yearlmp']]
if len(dftop) != len(dfbot):
print(market)
print('len(dftop) = {}'.format(len(dftop)))
print('len(dfbot) = {}'.format(len(dfbot)))
raise Exception('unequal lengths')
###### Baseline = curtailable
dicthist['Revenue_hist_default_t({})(curtail)'.format(market)] = dftop['Revenue_dispatched']
dicthist['CapacityFactor_hist_default_t({})(curtail)'.format(market)] = dftop['CapacityFactor_dispatched']
dicthist['Revenue_track(def)/fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
dftop['Revenue_dispatched'] / dfbot['Revenue_dispatched'])
dicthist['CapacityFactor_track(def)/fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
dftop['CapacityFactor_dispatched'] / dfbot['CapacityFactor_dispatched'])
dicthist['Value_hist_default_t({})(curtail)'.format(market)] = dftop['ValueAverage_dispatched']
dicthist['ValueFactor_hist_default_t({})(curtail)'.format(market)] = dftop['ValueFactor_dispatched']
###### Baseline = mustrun
dicthist['Revenue_track(def)/fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
dftop['Revenue_dispatched'] / dfbot['Revenue']) ### CHANGED dfbot from _Dispatched
dicthist['CapacityFactor_track(def)/fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
dftop['CapacityFactor_dispatched'] / dfbot['CapacityFactor']) ### CHANGED dfbot from _Dispatched
###############
### Differences
dicthist['Revenue_track(def)-fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
dftop['Revenue_dispatched'] - dfbot['Revenue_dispatched'])
dicthist['CapacityFactor_track(def)-fixed(optcf)_hist,{},curtail,baselinecurtail'.format(market)] = (
dftop['CapacityFactor_dispatched'] - dfbot['CapacityFactor_dispatched'])
dicthist['Revenue_track(def)-fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
dftop['Revenue_dispatched'] - dfbot['Revenue']) ### CHANGED dfbot from _Dispatched
dicthist['CapacityFactor_track(def)-fixed(optcf)_hist,{},curtail,baselinemustrun'.format(market)] = (
dftop['CapacityFactor_dispatched'] - dfbot['CapacityFactor']) ### CHANGED dfbot from _Dispatched
### Merge them
dfmerge = pd.concat(dicthist,axis=1)
for market in ['da','rt']:
###### Take ratios
##### Must-run
dfmerge['Rev_OptRev/OptCF_hist,{},f,mustrun'.format(market)] = (
dfmerge['Revenue_hist_optrev_f({})(mustrun)'.format(market)]
/ dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)])
dfmerge['CF_OptRev/OptCF_hist,{},f,mustrun'.format(market)] = (
dfmerge['CapacityFactor_hist_optrev_f({})(mustrun)'.format(market)]
/ dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)])
##### Curtailable
### Baseline = curtailable
dfmerge['Rev_OptRev/OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)]
/ dfmerge['Revenue_dispatched_hist_optcf_f({})(curtail)'.format(market)])
dfmerge['CF_OptRev/OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)]
/ dfmerge['CapacityFactor_dispatched_hist_optcf_f({})(curtail)'.format(market)])
### Baseline = mustrun
dfmerge['Rev_OptRev/OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)]
/ dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)]) ### CHANGED from _Dispatched, curtail
dfmerge['CF_OptRev/OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)]
/ dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)]) ### CHANGED from _Dispatched,curtail
###############
### Differences
##### Must-run
dfmerge['Rev_OptRev-OptCF_hist,{},f,mustrun'.format(market)] = (
dfmerge['Revenue_hist_optrev_f({})(mustrun)'.format(market)]
- dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)])
dfmerge['CF_OptRev-OptCF_hist,{},f,mustrun'.format(market)] = (
dfmerge['CapacityFactor_hist_optrev_f({})(mustrun)'.format(market)]
- dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)])
##### Curtailable
### Baseline = curtailable
dfmerge['Rev_OptRev-OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)]
- dfmerge['Revenue_dispatched_hist_optcf_f({})(curtail)'.format(market)])
dfmerge['CF_OptRev-OptCF_hist,{},f,curtail,baselinecurtail'.format(market)] = (
dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)]
- dfmerge['CapacityFactor_dispatched_hist_optcf_f({})(curtail)'.format(market)])
### Baseline = mustrun
dfmerge['Rev_OptRev-OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
dfmerge['Revenue_dispatched_hist_optrev_f({})(curtail)'.format(market)]
- dfmerge['Revenue_hist_optcf_f({})(mustrun)'.format(market)])
dfmerge['CF_OptRev-OptCF_hist,{},f,curtail,baselinemustrun'.format(market)] = (
dfmerge['CapacityFactor_dispatched_hist_optrev_f({})(curtail)'.format(market)]
- dfmerge['CapacityFactor_hist_optcf_f({})(mustrun)'.format(market)])
dfmerge.index = pd.MultiIndex.from_tuples(dfmerge.index, names=('ISO:Node', 'yearlmp'))
dfmerge.reset_index(inplace=True)
dfplot = dfplot.merge(dfmerge, on=['ISO:Node','yearlmp'])
/Users/patrickbrown/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:156: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version of pandas will change to not sort by default. To accept the future behavior, pass 'sort=False'. To retain the current behavior and silence the warning, pass 'sort=True'.
###### Fixed, DA and RT: Rev_curtail/Rev_mustrun
pricecutoff = '0'
systemtype = 'fixed'
##########
### Ratios
datum = 'Rev_curtail/Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
dfmerge = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.yearlmp.isin(years))
& (dfin_optorient_histsun.systemtype == systemtype)
& (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
& (dfin_optorient_histsun.Product == 'lmp')
& (dfin_optorient_histsun.mod0 == 'optcf')
& (dfin_optorient_histsun.mod1 == '0')
].copy()
### Get ratio
dfmerge[datum+'({})'.format(market)] = (
dfmerge['Revenue_dispatched'] / dfmerge['Revenue'])
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[datum+'({})'.format(market)]],
on=mergecols, how='outer')
datum = 'CF_curtail/CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
dfmerge = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.yearlmp.isin(years))
& (dfin_optorient_histsun.systemtype == systemtype)
& (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
& (dfin_optorient_histsun.Product == 'lmp')
& (dfin_optorient_histsun.mod0 == 'optcf')
& (dfin_optorient_histsun.mod1 == '0')
].copy()
### Get ratio
dfmerge[datum+'({})'.format(market)] = (
dfmerge['CapacityFactor_dispatched'] / dfmerge['CapacityFactor'])
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[datum+'({})'.format(market)]],
on=mergecols, how='outer')
###############
### Differences
datum = 'Rev_curtail-Rev_mustrun({})'.format(systemtype)
for market in ['da','rt']:
dfmerge = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.yearlmp.isin(years))
& (dfin_optorient_histsun.systemtype == systemtype)
& (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
& (dfin_optorient_histsun.Product == 'lmp')
& (dfin_optorient_histsun.mod0 == 'optcf')
& (dfin_optorient_histsun.mod1 == '0')
].copy()
### Get ratio
dfmerge[datum+'({})'.format(market)] = (
dfmerge['Revenue_dispatched'] - dfmerge['Revenue'])
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[datum+'({})'.format(market)]],
on=mergecols, how='outer')
datum = 'CF_curtail-CF_mustrun({})'.format(systemtype)
for market in ['da','rt']:
dfmerge = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.yearlmp.isin(years))
& (dfin_optorient_histsun.systemtype == systemtype)
& (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
& (dfin_optorient_histsun.Product == 'lmp')
& (dfin_optorient_histsun.mod0 == 'optcf')
& (dfin_optorient_histsun.mod1 == '0')
].copy()
### Get ratio
dfmerge[datum+'({})'.format(market)] = (
dfmerge['CapacityFactor_dispatched'] - dfmerge['CapacityFactor'])
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[datum+'({})'.format(market)]],
on=mergecols, how='outer')
############ Azimuth orientation for 1-axis-tracking sytems, vs CF-optimized fixed-tilt must-run
data = ['Revenue','CapacityFactor','ValueAverage','ValueFactor']
###### Must-run
for market in ['da','rt']:
dftop = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.yearlmp.isin(years))
& (dfin_optorient_histsun.systemtype == 'track')
& (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
& (dfin_optorient_histsun.Product == 'lmp')
& (dfin_optorient_histsun.mod0 == 'optrev')
& (dfin_optorient_histsun.mod1 == 'azopt'),
mergecols+data
].copy()
dfbot = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.systemtype == 'fixed')
& (dfin_optorient_histsun.yearlmp.isin(years))
& (dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.mod0 == 'optcf')
& (dfin_optorient_histsun.mod1 == '0'),
mergecols+data
].copy()
if len(dftop) != len(dfbot):
print(market)
print('len(dftop) = {}'.format(len(dftop)))
print('len(dfbot) = {}'.format(len(dfbot)))
raise Exception('unequal lengths')
dfmerge = dftop.merge(
dfbot, on=['ISOwecc','ISO:Node','yearlmp'],
suffixes=('_track,optrev,{},mustrun'.format(market), '_fixed,optcf,{},mustrun'.format(market)),
)
### Calculate ratios
dfmerge['CF_track,optrev,{},mustrun/CF_fixed,optcf,{},mustrun'.format(market,market)] = (
dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
/ dfmerge['CapacityFactor_fixed,optcf,{},mustrun'.format(market)]
)
dfmerge['Rev_track,optrev,{},mustrun/Rev_fixed,optcf,{},mustrun'.format(market,market)] = (
dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
/ dfmerge['Revenue_fixed,optcf,{},mustrun'.format(market)]
)
###############
### Differences
dfmerge['CF_track,optrev,{},mustrun-CF_fixed,optcf,{},mustrun'.format(market,market)] = (
dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
- dfmerge['CapacityFactor_fixed,optcf,{},mustrun'.format(market)]
)
dfmerge['Rev_track,optrev,{},mustrun-Rev_fixed,optcf,{},mustrun'.format(market,market)] = (
dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
- dfmerge['Revenue_fixed,optcf,{},mustrun'.format(market)]
)
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[
'CF_track,optrev,{},mustrun/CF_fixed,optcf,{},mustrun'.format(market,market),
'Rev_track,optrev,{},mustrun/Rev_fixed,optcf,{},mustrun'.format(market,market),
'CF_track,optrev,{},mustrun-CF_fixed,optcf,{},mustrun'.format(market,market),
'Rev_track,optrev,{},mustrun-Rev_fixed,optcf,{},mustrun'.format(market,market),
]],
on=mergecols, how='outer',
)
############ Azimuth orientation for 1-axis-tracking sytems, vs default track must-run
data = ['Revenue','CapacityFactor','ValueAverage','ValueFactor']
###### Must-run
for market in ['da','rt']:
dftop = dfin_optorient_histsun.loc[
(dfin_optorient_histsun.market == market)
& (dfin_optorient_histsun.yearlmp.isin(years))
& (dfin_optorient_histsun.systemtype == 'track')
& (dfin_optorient_histsun.yearsun == dfin_optorient_histsun.yearlmp)
& (dfin_optorient_histsun.Product == 'lmp')
& (dfin_optorient_histsun.mod0 == 'optrev')
& (dfin_optorient_histsun.mod1 == 'azopt'),
mergecols+data
].copy()
dfbot = dfin.loc[
(dfin.market == market)
& (dfin.yearlmp.isin(years))
& (dfin.program == 'PVvalueV8')
& (dfin.systemtype == 'track')
& (dfin.yearsun == dfin.yearlmp)
& (dfin.Product == 'lmp'),
mergecols+data
].copy()
if len(dftop) != len(dfbot):
print(market)
print('len(dftop) = {}'.format(len(dftop)))
print('len(dfbot) = {}'.format(len(dfbot)))
raise Exception('unequal lengths')
dfmerge = dftop.merge(
dfbot, on=['ISOwecc','ISO:Node','yearlmp'],
suffixes=('_track,optrev,{},mustrun'.format(market), '_track,default,{},mustrun'.format(market)),
)
### Calculate ratios
dfmerge['CF_track,optrev,{},mustrun/CF_track,default,{},mustrun'.format(market,market)] = (
dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
/ dfmerge['CapacityFactor_track,default,{},mustrun'.format(market)]
)
dfmerge['Rev_track,optrev,{},mustrun/Rev_track,default,{},mustrun'.format(market,market)] = (
dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
/ dfmerge['Revenue_track,default,{},mustrun'.format(market)]
)
###############
### Differences
dfmerge['CF_track,optrev,{},mustrun-CF_track,default,{},mustrun'.format(market,market)] = (
dfmerge['CapacityFactor_track,optrev,{},mustrun'.format(market)]
- dfmerge['CapacityFactor_track,default,{},mustrun'.format(market)]
)
dfmerge['Rev_track,optrev,{},mustrun-Rev_track,default,{},mustrun'.format(market,market)] = (
dfmerge['Revenue_track,optrev,{},mustrun'.format(market)]
- dfmerge['Revenue_track,default,{},mustrun'.format(market)]
)
### Merge with rest of plot data
dfplot = dfplot.merge(
dfmerge[mergecols+[
'CF_track,optrev,{},mustrun/CF_track,default,{},mustrun'.format(market,market),
'Rev_track,optrev,{},mustrun/Rev_track,default,{},mustrun'.format(market,market),
'CF_track,optrev,{},mustrun-CF_track,default,{},mustrun'.format(market,market),
'Rev_track,optrev,{},mustrun-Rev_track,default,{},mustrun'.format(market,market),
]],
on=mergecols, how='outer',
)
####### Azimuth optimization for tracking systems
systemtype = 'track'
pricecutoff = 'no'
yearsun = 'tmy'
program = 'PVvalueOptV4'
dfmerge = dfin.loc[
(dfin.program==program)
&(dfin.systemtype==systemtype)
&(dfin.pricecutoff==pricecutoff)
&(dfin.yearsun==yearsun)
&(dfin.market=='da'),
mergecols+['OptCF_Azimuth']
].rename(columns={'OptCF_Azimuth':'OptCF_Azimuth(track)'})
for market in ['da','rt']:
dfmerge = dfmerge.merge(
dfin.loc[
(dfin.program==program)
&(dfin.systemtype==systemtype)
&(dfin.pricecutoff==pricecutoff)
&(dfin.yearsun==yearsun)
&(dfin.market==market),
mergecols+['OptRev_Azimuth']
].rename(columns={'OptRev_Azimuth':'OptRev_Azimuth(track)({})(mustrun)'.format(market)}),
on=mergecols, how='outer')
for datum in ['OptCF_Azimuth(track)', 'OptRev_Azimuth(track)(da)(mustrun)',
'OptRev_Azimuth(track)(rt)(mustrun)', ]:
dfmerge[datum] = dfmerge[datum].map(lambda x: x + 180 if x < 90 else x)
dfplot = dfplot.merge(dfmerge, on=mergecols, how='outer').copy()
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
'CapacityFactor_hist_optcf_f(da)(mustrun)',
'Revenue_hist_optcf_f(da)(mustrun)',
'Revenue_hist_optcf_f(rt)(mustrun)',
'ValueAverage_hist_optcf_f(da)(mustrun)',
'ValueAverage_hist_optcf_f(rt)(mustrun)',
'ValueFactor_hist_optcf_f(da)(mustrun)',
'ValueFactor_hist_optcf_f(rt)(mustrun)',
]
colindex = [0, 1, 1, 2, 2, 3, 3]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))
### Column-indexed parameters
ncols = 4
# cols = [0,1,2,3]
ylim = [
[0,0.3],
[0,180], #[0,210],
[0,90], #[0,110],
[0.5,1.7], #[0.6,1.8],
]
ylabel = [
'CF',
'Revenue',
'Value',
'VF',
]
note = [
'[fraction]',
'[$/kWac-yr]',
'[$/MWh]',
'[fraction]',
]
gridspec_kw = {'width_ratios': [1, 2, 2, 2]}
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
# medianmarker='_', mediansize=10, medianfacecolor='k'
)
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
ax[(row,col)].set_xlim(2009.4,2018)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=1.2, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
### Add 1-line for VF
ax[(row,-1)].axhline(1, lw=0.25, c='0.5')
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(0,-1)].legend(
handles=patches, loc='upper right', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Quantities, CF-optimized fixed-tilt, must-run', weight='bold', y=1.07, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/pvvm/plots.py:495: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/pvvm/plots.py:495: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvtos_FINAL/pvvm/plots.py:495: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
print(datum)
display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
| CapacityFactor_hist_optcf_f(da)(mustrun) | Revenue_hist_optcf_f(da)(mustrun) | Revenue_hist_optcf_f(rt)(mustrun) | ValueAverage_hist_optcf_f(da)(mustrun) | ValueAverage_hist_optcf_f(rt)(mustrun) | ValueFactor_hist_optcf_f(da)(mustrun) | ValueFactor_hist_optcf_f(rt)(mustrun) | |
|---|---|---|---|---|---|---|---|
| count | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 |
| mean | 0.246188 | 59.642007 | 53.883991 | 27.708089 | 25.040576 | 0.801583 | 0.788523 |
| std | 0.011704 | 6.841004 | 7.212112 | 3.342419 | 3.514949 | 0.062013 | 0.079220 |
| min | 0.187778 | 28.469182 | 27.826508 | 12.993617 | 13.054663 | 0.451965 | 0.469798 |
| 2.5% | 0.225073 | 45.610871 | 40.140121 | 20.772181 | 18.265009 | 0.671348 | 0.639226 |
| 25% | 0.240347 | 56.143990 | 49.279463 | 25.543496 | 22.333303 | 0.761790 | 0.728410 |
| 50% | 0.245543 | 59.215096 | 52.814589 | 27.703784 | 25.011672 | 0.816433 | 0.811701 |
| 75% | 0.252588 | 63.347934 | 58.849791 | 29.722195 | 27.819860 | 0.843112 | 0.851000 |
| 97.5% | 0.274404 | 75.239969 | 69.971012 | 34.382648 | 31.960051 | 0.915864 | 0.905468 |
| max | 0.282761 | 82.950998 | 74.620503 | 40.043755 | 35.075066 | 0.976529 | 0.937086 |
median
| CapacityFactor_hist_optcf_f(da)(mustrun) | Revenue_hist_optcf_f(da)(mustrun) | Revenue_hist_optcf_f(rt)(mustrun) | ValueAverage_hist_optcf_f(da)(mustrun) | ValueAverage_hist_optcf_f(rt)(mustrun) | ValueFactor_hist_optcf_f(da)(mustrun) | ValueFactor_hist_optcf_f(rt)(mustrun) | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | |||||||||||||||||||||||||||||||||||||||||||||||||
| 2010 | 0.230972 | NaN | 0.193482 | 0.198847 | 0.194502 | 0.198551 | NaN | 91.630257 | NaN | 105.497863 | 77.500738 | 112.758798 | 91.734709 | NaN | 94.208201 | NaN | NaN | 74.620116 | 117.453727 | 90.926518 | NaN | 45.325221 | NaN | 62.599238 | 43.293228 | 66.853404 | 54.546457 | NaN | 46.945908 | NaN | NaN | 41.735509 | 67.335810 | 53.880279 | NaN | 1.109605 | NaN | 1.138015 | 1.219608 | 1.152851 | 1.165117 | NaN | 1.062646 | NaN | NaN | 1.222089 | 1.144497 | 1.177101 | NaN |
| 2011 | 0.243936 | 0.234866 | 0.190098 | 0.189981 | 0.189608 | 0.188582 | NaN | 86.103130 | 163.255041 | 94.790453 | 69.637654 | 102.834552 | 89.623317 | NaN | 80.671016 | 147.202962 | 95.315083 | 67.368666 | 102.822357 | 90.472253 | NaN | 40.417082 | 79.033268 | 56.953720 | 41.132766 | 61.773388 | 54.993100 | NaN | 37.881238 | 71.076056 | 57.200568 | 39.981424 | 62.023122 | 56.037231 | NaN | 1.193562 | 1.591313 | 1.128503 | 1.214730 | 1.150326 | 1.207836 | NaN | 1.120612 | 1.533552 | 1.126617 | 1.212205 | 1.177433 | 1.224964 | NaN |
| 2012 | 0.244936 | 0.232577 | 0.199708 | 0.210066 | 0.196966 | 0.203782 | NaN | 76.214457 | 82.804797 | 75.495416 | 60.433441 | 79.442607 | 70.136512 | NaN | 76.621147 | 70.834097 | 75.994444 | 59.939321 | 81.261827 | 71.671996 | NaN | 35.591168 | 40.427457 | 43.116905 | 33.426375 | 46.395278 | 40.112320 | NaN | 35.546166 | 34.519891 | 43.572244 | 33.230227 | 47.427459 | 40.858529 | NaN | 1.139462 | 1.376841 | 1.115548 | 1.175683 | 1.150527 | 1.165354 | NaN | 1.145248 | 1.290854 | 1.128399 | 1.190303 | 1.184282 | 1.177129 | NaN |
| 2013 | 0.258688 | 0.226295 | 0.198371 | 0.195050 | 0.195548 | 0.194807 | NaN | 107.022227 | 79.332946 | 109.051458 | 61.371157 | 102.792563 | 72.538165 | NaN | 98.699543 | 74.592091 | 108.962743 | 60.310346 | 103.703793 | 73.717128 | NaN | 47.221461 | 39.773405 | 63.002813 | 36.261442 | 60.355995 | 44.514600 | NaN | 43.754006 | 37.518670 | 62.770983 | 35.717012 | 61.095318 | 44.325946 | NaN | 1.059504 | 1.182064 | 1.077531 | 1.137146 | 1.133439 | 1.153043 | NaN | 1.041000 | 1.160513 | 1.077664 | 1.135742 | 1.166363 | 1.164494 | NaN |
| 2014 | 0.245462 | 0.222564 | 0.194251 | 0.204298 | 0.189929 | 0.193770 | NaN | 108.886657 | 88.954743 | 119.572237 | 76.334670 | 113.466331 | 87.085898 | NaN | 99.847372 | 81.070067 | 111.806277 | 72.871556 | 108.451109 | 87.549573 | NaN | 50.624526 | 45.230950 | 70.751555 | 43.106767 | 68.232393 | 52.933508 | NaN | 47.074163 | 41.357884 | 66.180022 | 41.062071 | 66.128568 | 53.510857 | NaN | 1.022801 | 1.137982 | 1.069726 | 1.101950 | 1.082699 | 1.086669 | NaN | 0.992912 | 1.087337 | 1.027523 | 1.069602 | 1.110393 | 1.110727 | NaN |
| 2015 | 0.248991 | 0.213987 | 0.201143 | 0.203341 | 0.199722 | 0.196122 | 0.226895 | 73.239214 | 67.027475 | 81.934695 | 55.204096 | 79.654977 | 67.236213 | 60.160760 | 70.537787 | 53.928449 | 77.476387 | 54.091967 | 76.588783 | 64.386584 | 45.203972 | 33.848354 | 35.470877 | 46.708785 | 31.837814 | 45.406957 | 39.430827 | 30.262247 | 33.193210 | 28.553451 | 44.117595 | 30.718069 | 43.297521 | 37.790873 | 23.187285 | 0.993296 | 1.343341 | 1.087579 | 1.146485 | 1.152415 | 1.164359 | 0.955955 | 0.976788 | 1.196270 | 1.054393 | 1.156216 | 1.129825 | 1.144031 | 1.069051 |
| 2016 | 0.248407 | 0.227279 | 0.209589 | 0.212410 | 0.206703 | 0.201994 | 0.233462 | 59.331690 | 59.706667 | 61.372094 | 55.933880 | 61.697934 | 57.699422 | 50.692495 | 55.657016 | 55.293798 | 57.768955 | 54.724383 | 59.509091 | 57.117897 | 40.173297 | 27.562865 | 29.540111 | 33.328241 | 30.620093 | 33.805654 | 33.158103 | 24.467022 | 25.910069 | 27.542636 | 31.430099 | 29.891158 | 32.537043 | 32.603484 | 19.214541 | 0.919551 | 1.311012 | 1.099718 | 1.173928 | 1.157107 | 1.169964 | 0.872516 | 0.870209 | 1.276788 | 1.071098 | 1.177482 | 1.164329 | 1.165288 | 0.850056 |
| 2017 | 0.245543 | 0.226272 | 0.199129 | 0.208934 | 0.197039 | 0.199729 | 0.254154 | 59.215096 | 58.688937 | 60.681659 | 57.310864 | 60.449408 | 57.115360 | 50.727470 | 52.814589 | 55.402269 | 57.717520 | 57.560303 | 56.736561 | 57.307566 | 37.422478 | 27.703784 | 29.010892 | 34.789771 | 32.132992 | 35.720484 | 33.341268 | 24.026835 | 25.011672 | 27.621548 | 33.125203 | 32.137731 | 33.042092 | 33.385809 | 19.410569 | 0.816433 | 1.232272 | 1.048331 | 1.173494 | 1.118062 | 1.137926 | 0.754621 | 0.811701 | 1.184371 | 0.988141 | 1.191962 | 1.051551 | 1.144193 | 0.741065 |
max
| CapacityFactor_hist_optcf_f(da)(mustrun) | Revenue_hist_optcf_f(da)(mustrun) | Revenue_hist_optcf_f(rt)(mustrun) | ValueAverage_hist_optcf_f(da)(mustrun) | ValueAverage_hist_optcf_f(rt)(mustrun) | ValueFactor_hist_optcf_f(da)(mustrun) | ValueFactor_hist_optcf_f(rt)(mustrun) | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | |||||||||||||||||||||||||||||||||||||||||||||||||
| 2010 | 0.277866 | NaN | 0.207146 | 0.225963 | 0.206615 | 0.228378 | NaN | 116.851289 | NaN | 117.537375 | 113.244225 | 162.037417 | 174.184442 | NaN | 130.018708 | NaN | NaN | 114.945681 | 160.141739 | 164.998095 | NaN | 51.138572 | NaN | 68.874514 | 70.606498 | 91.530305 | 90.246885 | NaN | 56.869385 | NaN | NaN | 71.667337 | 89.056471 | 85.487338 | NaN | 1.139584 | NaN | 1.153784 | 1.304819 | 1.198685 | 1.357076 | NaN | 1.179287 | NaN | NaN | 1.370531 | 1.210511 | 1.333850 | NaN |
| 2011 | 0.285192 | 0.279049 | 0.205588 | 0.223398 | 0.206670 | 0.222986 | NaN | 106.351969 | 426.367353 | 105.779767 | 89.751867 | 145.127694 | 152.744892 | NaN | 99.718389 | 326.267108 | 108.772527 | 87.924704 | 153.641869 | 142.928318 | NaN | 46.800609 | 180.349037 | 61.840130 | 56.492077 | 83.694936 | 80.060863 | NaN | 44.866865 | 138.007656 | 61.772892 | 55.342015 | 87.563832 | 74.915530 | NaN | 1.253313 | 1.779045 | 1.147259 | 1.301418 | 1.263254 | 1.368157 | NaN | 1.237704 | 1.792053 | 1.155443 | 1.381830 | 1.289410 | 1.360069 | NaN |
| 2012 | 0.281405 | 0.273209 | 0.207713 | 0.231437 | 0.208879 | 0.218694 | NaN | 120.540637 | 778.858911 | 87.800296 | 74.783118 | 122.350396 | 137.166419 | NaN | 120.537862 | 694.822127 | 91.392987 | 82.178646 | 123.838466 | 138.588866 | NaN | 54.646675 | 336.886631 | 49.998567 | 47.209960 | 66.694580 | 71.803614 | NaN | 54.572420 | 300.006977 | 51.676310 | 51.878695 | 68.664584 | 72.548234 | NaN | 1.367523 | 1.940491 | 1.173878 | 1.326116 | 1.341058 | 1.272452 | NaN | 1.404304 | 2.121927 | 1.213145 | 1.361444 | 1.310590 | 1.285923 | NaN |
| 2013 | 0.283820 | 0.269719 | 0.206275 | 0.217156 | 0.210662 | 0.214248 | NaN | 126.315515 | 295.869659 | 124.496771 | 78.324788 | 156.028937 | 122.917740 | NaN | 115.165879 | 219.394761 | 121.951244 | 79.830470 | 204.811975 | 125.289742 | NaN | 54.949088 | 128.588504 | 69.726218 | 46.239649 | 86.942269 | 67.635080 | NaN | 51.113866 | 95.318513 | 68.044938 | 46.308729 | 114.125099 | 68.940266 | NaN | 1.131657 | 1.813841 | 1.130427 | 1.253033 | 1.191331 | 1.282994 | NaN | 1.122203 | 1.701891 | 1.096285 | 1.282900 | 1.274053 | 1.364994 | NaN |
| 2014 | 0.279916 | 0.265088 | 0.203394 | 0.223683 | 0.206444 | 0.215460 | NaN | 143.808370 | 292.514372 | 127.472113 | 114.593187 | 144.684377 | 193.165455 | NaN | 132.967096 | 197.115729 | 120.849700 | 114.276118 | 161.456743 | 187.543788 | NaN | 65.550925 | 137.372822 | 75.338511 | 64.177441 | 84.260185 | 104.543246 | NaN | 61.328208 | 89.325593 | 70.148506 | 63.999868 | 91.952168 | 101.926090 | NaN | 1.075579 | 1.664182 | 1.153818 | 1.249215 | 1.131118 | 1.167482 | NaN | 1.079433 | 1.532755 | 1.128290 | 1.252162 | 1.173316 | 1.314645 | NaN |
| 2015 | 0.280158 | 0.255952 | 0.209564 | 0.219845 | 0.211239 | 0.211704 | 0.260411 | 111.083014 | 104.810763 | 101.922449 | 71.453293 | 109.363575 | 132.634029 | 68.143543 | 110.014158 | 87.213937 | 83.699451 | 70.988370 | 105.433792 | 128.250100 | 52.701057 | 51.850500 | 49.667198 | 56.960907 | 39.863414 | 61.837104 | 72.657013 | 35.123601 | 50.599382 | 48.368216 | 48.159494 | 40.116250 | 63.448290 | 70.404004 | 25.880763 | 1.085500 | 1.534029 | 1.202821 | 1.244111 | 1.305214 | 1.302858 | 1.045935 | 1.114281 | 1.528232 | 1.109420 | 1.328237 | 1.516640 | 1.457411 | 1.142447 |
| 2016 | 0.281632 | 0.262511 | 0.219156 | 0.231624 | 0.222363 | 0.225568 | 0.276496 | 67.441523 | 128.198562 | 66.022374 | 73.002232 | 91.640258 | 120.496917 | 62.088274 | 64.641734 | 141.959905 | 62.485980 | 72.925825 | 98.416609 | 111.255032 | 58.409003 | 33.016643 | 65.213393 | 35.491711 | 39.262600 | 52.523487 | 62.346818 | 28.823508 | 33.178101 | 72.213658 | 33.983111 | 39.221507 | 56.407343 | 57.564935 | 26.436307 | 1.000511 | 1.727105 | 1.131670 | 1.266791 | 1.626424 | 1.300367 | 0.980711 | 0.971997 | 1.796781 | 1.214950 | 1.365266 | 1.703000 | 1.290563 | 1.045332 |
| 2017 | 0.282761 | 0.267850 | 0.207351 | 0.227381 | 0.209204 | 0.226219 | 0.276142 | 82.950998 | 104.163372 | 64.060605 | 82.061544 | 86.033215 | 87.835021 | 63.711292 | 74.620503 | 128.990140 | 62.265714 | 86.511683 | 77.479656 | 80.494622 | 60.736481 | 40.043755 | 52.523915 | 37.047420 | 43.651335 | 47.783136 | 47.560762 | 32.996176 | 35.075066 | 63.937091 | 35.346495 | 44.407004 | 43.032461 | 47.245435 | 28.116618 | 0.976529 | 1.507225 | 1.207329 | 1.310353 | 1.405340 | 1.286612 | 0.940674 | 0.937086 | 1.620960 | 1.482201 | 1.371905 | 1.447970 | 1.287909 | 1.073048 |
CapacityFactor_hist_optcf_f(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 0.232652 | 0.245190 | 0.244435 | 0.258043 | 0.246584 | 0.248396 | 0.247330 | 0.246188 | 0.237353 | 0.233811 | 0.229137 | 0.224395 | 0.216660 | 0.229445 | 0.228360 | 0.190555 | 0.188857 | 0.197713 | 0.195224 | 0.191377 | 0.199974 | 0.207266 | 0.197753 | 0.197341 | 0.190831 | 0.207693 | 0.194743 | 0.202065 | 0.201792 | 0.209760 | 0.207154 | 0.189120 | 0.186542 | 0.193139 | 0.189843 | 0.187356 | 0.196382 | 0.203475 | 0.193788 | 0.198312 | 0.190271 | 0.202780 | 0.193415 | 0.193326 | 0.195856 | 0.203491 | 0.199148 | 0.221632 | 0.236713 | 0.238426 |
| std | 0.014412 | 0.012606 | 0.012300 | 0.008757 | 0.012197 | 0.009014 | 0.012372 | 0.011704 | 0.010747 | 0.010504 | 0.012283 | 0.011707 | 0.010886 | 0.009475 | 0.010145 | 0.009408 | 0.006664 | 0.005534 | 0.008408 | 0.007473 | 0.004207 | 0.006670 | 0.005155 | 0.009304 | 0.009098 | 0.010889 | 0.011114 | 0.012111 | 0.007098 | 0.010169 | 0.008433 | 0.014195 | 0.012401 | 0.008876 | 0.013602 | 0.010603 | 0.008383 | 0.009424 | 0.009766 | 0.010938 | 0.012499 | 0.007841 | 0.010048 | 0.009472 | 0.006770 | 0.008252 | 0.008816 | 0.015364 | 0.024022 | 0.030500 |
| min | 0.173918 | 0.193692 | 0.171110 | 0.203060 | 0.195900 | 0.199669 | 0.196407 | 0.187778 | 0.219438 | 0.212187 | 0.206964 | 0.197210 | 0.198289 | 0.210270 | 0.208513 | 0.161775 | 0.168907 | 0.181200 | 0.169681 | 0.169265 | 0.185596 | 0.183795 | 0.178488 | 0.174960 | 0.170079 | 0.176083 | 0.165189 | 0.154668 | 0.176217 | 0.183681 | 0.175909 | 0.164429 | 0.163120 | 0.174881 | 0.161536 | 0.166203 | 0.178952 | 0.185901 | 0.173762 | 0.169249 | 0.162624 | 0.177008 | 0.160435 | 0.169993 | 0.177850 | 0.183756 | 0.175545 | 0.175029 | 0.160074 | 0.165343 |
| 2.5% | 0.208335 | 0.222666 | 0.223061 | 0.238273 | 0.225968 | 0.234869 | 0.219041 | 0.225073 | 0.224218 | 0.217323 | 0.210992 | 0.204041 | 0.202336 | 0.215856 | 0.214852 | 0.168085 | 0.174743 | 0.185035 | 0.175755 | 0.174051 | 0.188857 | 0.191418 | 0.184829 | 0.180039 | 0.176544 | 0.183455 | 0.171857 | 0.176615 | 0.183624 | 0.189082 | 0.188677 | 0.166983 | 0.165511 | 0.177199 | 0.165342 | 0.170124 | 0.179592 | 0.186832 | 0.177004 | 0.178770 | 0.170649 | 0.185958 | 0.172550 | 0.174672 | 0.182475 | 0.189725 | 0.182668 | 0.184294 | 0.177503 | 0.177094 |
| 25% | 0.224480 | 0.238225 | 0.239986 | 0.255004 | 0.238837 | 0.242646 | 0.240335 | 0.240347 | 0.231548 | 0.228076 | 0.222471 | 0.217984 | 0.210657 | 0.223966 | 0.222721 | 0.188044 | 0.185747 | 0.194775 | 0.191531 | 0.188172 | 0.198717 | 0.204839 | 0.196382 | 0.188936 | 0.185344 | 0.198671 | 0.186321 | 0.193984 | 0.199122 | 0.203121 | 0.202481 | 0.174682 | 0.173456 | 0.184431 | 0.176722 | 0.176092 | 0.187735 | 0.194185 | 0.183894 | 0.190996 | 0.181061 | 0.197812 | 0.187423 | 0.187713 | 0.190953 | 0.197538 | 0.193094 | 0.214574 | 0.223098 | 0.221056 |
| 50% | 0.230972 | 0.243936 | 0.244936 | 0.258688 | 0.245462 | 0.248991 | 0.248407 | 0.245543 | 0.234866 | 0.232577 | 0.226295 | 0.222564 | 0.213987 | 0.227279 | 0.226272 | 0.193482 | 0.190098 | 0.199708 | 0.198371 | 0.194251 | 0.201143 | 0.209589 | 0.199129 | 0.198847 | 0.189981 | 0.210066 | 0.195050 | 0.204298 | 0.203341 | 0.212410 | 0.208934 | 0.194502 | 0.189608 | 0.196966 | 0.195548 | 0.189929 | 0.199722 | 0.206703 | 0.197039 | 0.198551 | 0.188582 | 0.203782 | 0.194807 | 0.193770 | 0.196122 | 0.201994 | 0.199729 | 0.226895 | 0.233462 | 0.254154 |
| 75% | 0.241740 | 0.252387 | 0.249604 | 0.262092 | 0.255667 | 0.252410 | 0.255109 | 0.252588 | 0.239234 | 0.236262 | 0.231392 | 0.228012 | 0.218764 | 0.232245 | 0.230555 | 0.197123 | 0.193722 | 0.201651 | 0.201684 | 0.196809 | 0.202754 | 0.211578 | 0.201086 | 0.203396 | 0.195544 | 0.216550 | 0.204496 | 0.211216 | 0.206093 | 0.216798 | 0.213269 | 0.202244 | 0.198042 | 0.199148 | 0.201630 | 0.195815 | 0.202614 | 0.212031 | 0.202601 | 0.204765 | 0.197942 | 0.208652 | 0.201026 | 0.199638 | 0.201261 | 0.210079 | 0.204569 | 0.231159 | 0.260125 | 0.262199 |
| 97.5% | 0.269139 | 0.277368 | 0.271449 | 0.275072 | 0.271443 | 0.269770 | 0.272408 | 0.274404 | 0.271558 | 0.264260 | 0.263887 | 0.256828 | 0.247668 | 0.257023 | 0.260103 | 0.201700 | 0.198671 | 0.204188 | 0.205412 | 0.200667 | 0.205269 | 0.215520 | 0.204218 | 0.212018 | 0.214101 | 0.223600 | 0.210027 | 0.218603 | 0.212175 | 0.224108 | 0.219534 | 0.206602 | 0.203847 | 0.206583 | 0.207266 | 0.204395 | 0.209293 | 0.215591 | 0.205754 | 0.221948 | 0.217520 | 0.215017 | 0.209066 | 0.210115 | 0.206933 | 0.219446 | 0.216040 | 0.243591 | 0.267128 | 0.269153 |
| max | 0.277866 | 0.285192 | 0.281405 | 0.283820 | 0.279916 | 0.280158 | 0.281632 | 0.282761 | 0.279049 | 0.273209 | 0.269719 | 0.265088 | 0.255952 | 0.262511 | 0.267850 | 0.207146 | 0.205588 | 0.207713 | 0.206275 | 0.203394 | 0.209564 | 0.219156 | 0.207351 | 0.225963 | 0.223398 | 0.231437 | 0.217156 | 0.223683 | 0.219845 | 0.231624 | 0.227381 | 0.206615 | 0.206670 | 0.208879 | 0.210662 | 0.206444 | 0.211239 | 0.222363 | 0.209204 | 0.228378 | 0.222986 | 0.218694 | 0.214248 | 0.215460 | 0.211704 | 0.225568 | 0.226219 | 0.260411 | 0.276496 | 0.276142 |
Revenue_hist_optcf_f(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 92.017992 | 86.390811 | 77.974030 | 107.271957 | 110.104298 | 73.928335 | 58.816583 | 59.642007 | 164.868145 | 91.047394 | 85.353546 | 92.596227 | 68.501629 | 60.761364 | 61.179336 | 104.678846 | 94.423800 | 75.294163 | 107.608886 | 118.502668 | 81.864221 | 60.588686 | 59.961438 | 75.715618 | 68.528698 | 60.194795 | 60.332129 | 76.962169 | 54.648883 | 56.121925 | 59.714467 | 111.509157 | 103.250748 | 81.746568 | 101.160582 | 106.310234 | 75.319010 | 60.498390 | 58.554497 | 99.902506 | 92.626753 | 72.116685 | 75.925413 | 92.390270 | 69.615453 | 60.004503 | 58.384859 | 59.665230 | 50.825820 | 50.237658 |
| std | 6.005148 | 5.572234 | 8.389878 | 6.446090 | 9.415272 | 7.362007 | 3.933406 | 6.841004 | 14.395855 | 39.251020 | 24.425163 | 16.505927 | 5.253532 | 4.789278 | 7.581061 | 6.892035 | 4.123885 | 3.448186 | 6.184023 | 5.252478 | 3.498104 | 2.915279 | 2.681080 | 7.830648 | 7.746373 | 6.569510 | 5.763363 | 9.851860 | 5.391374 | 6.321088 | 7.773952 | 27.356155 | 25.868541 | 18.001874 | 28.357297 | 20.671382 | 15.944648 | 13.746859 | 12.728755 | 19.817720 | 14.544858 | 6.797960 | 8.818024 | 14.604734 | 9.496760 | 7.983352 | 4.964799 | 2.986022 | 3.773570 | 3.695830 |
| min | 71.274534 | 61.611789 | 50.573090 | 84.319527 | 87.988728 | 53.915681 | 41.329243 | 28.469182 | 125.249880 | 74.047564 | 69.603852 | 74.660346 | 60.389228 | 52.059542 | 43.690718 | 83.379435 | 81.050723 | 66.660673 | 87.551765 | 100.408828 | 70.252272 | 48.377149 | 42.338154 | 53.946476 | 51.359806 | 44.469473 | 43.787464 | 43.895560 | 40.793988 | 36.350701 | 43.450319 | 73.250614 | 66.254367 | 58.395045 | 60.035540 | 58.002171 | 42.424838 | 34.917746 | 34.507602 | 54.537289 | 64.068315 | 55.295119 | 59.018591 | 63.240606 | 43.773042 | 38.406954 | 47.364189 | 49.927987 | 38.471109 | 41.306530 |
| 2.5% | 79.290844 | 74.528975 | 63.876227 | 94.882491 | 94.949389 | 63.312943 | 51.505725 | 45.610871 | 150.893603 | 76.624588 | 74.536295 | 81.245414 | 63.339557 | 56.316918 | 53.613932 | 88.370756 | 85.970329 | 68.715172 | 95.341394 | 107.319311 | 73.688511 | 52.393110 | 52.678298 | 59.060405 | 52.324227 | 46.210260 | 47.576138 | 59.280506 | 44.501709 | 40.687911 | 46.665420 | 74.091354 | 68.585434 | 60.033661 | 61.755615 | 71.853762 | 49.204100 | 35.473203 | 35.440768 | 76.191814 | 74.133712 | 63.693213 | 65.506673 | 73.900233 | 58.292767 | 50.443675 | 50.657739 | 52.259418 | 41.771970 | 43.556548 |
| 25% | 88.526639 | 83.447599 | 73.198409 | 102.891764 | 103.476367 | 68.846922 | 56.010668 | 56.143990 | 159.963083 | 81.285597 | 77.790668 | 86.510929 | 65.729020 | 58.362987 | 56.056007 | 101.603689 | 91.566926 | 73.214170 | 102.935325 | 116.177159 | 80.610123 | 59.169902 | 59.516981 | 71.755705 | 63.132385 | 55.691935 | 56.457682 | 71.328454 | 51.796658 | 52.512388 | 54.348788 | 80.625940 | 76.057109 | 64.589622 | 69.007232 | 82.451967 | 59.395270 | 48.659040 | 46.578631 | 81.776810 | 78.696750 | 67.415090 | 68.824927 | 80.576582 | 63.582569 | 55.694614 | 55.164823 | 58.272293 | 48.675114 | 47.887450 |
| 50% | 91.630257 | 86.103130 | 76.214457 | 107.022227 | 108.886657 | 73.239214 | 59.331690 | 59.215096 | 163.255041 | 82.804797 | 79.332946 | 88.954743 | 67.027475 | 59.706667 | 58.688937 | 105.497863 | 94.790453 | 75.495416 | 109.051458 | 119.572237 | 81.934695 | 61.372094 | 60.681659 | 77.500738 | 69.637654 | 60.433441 | 61.371157 | 76.334670 | 55.204096 | 55.933880 | 57.310864 | 112.758798 | 102.834552 | 79.442607 | 102.792563 | 113.466331 | 79.654977 | 61.697934 | 60.449408 | 91.734709 | 89.623317 | 70.136512 | 72.538165 | 87.085898 | 67.236213 | 57.699422 | 57.115360 | 60.160760 | 50.692495 | 50.727470 |
| 75% | 96.640049 | 90.556029 | 82.294617 | 112.096901 | 114.976130 | 77.235285 | 61.898480 | 63.347934 | 165.906664 | 84.229868 | 82.340444 | 91.402491 | 69.417171 | 62.070897 | 63.316588 | 109.781746 | 97.608945 | 77.325865 | 111.836139 | 122.245235 | 83.360243 | 62.435779 | 61.347449 | 80.095639 | 72.697239 | 64.499733 | 64.399260 | 81.505728 | 58.226821 | 60.867060 | 64.973072 | 135.822436 | 129.754458 | 95.809650 | 129.957604 | 119.131628 | 85.857255 | 69.880305 | 70.291425 | 118.716239 | 106.429179 | 75.719462 | 82.894914 | 103.521574 | 75.098884 | 63.759995 | 61.268355 | 61.179078 | 53.155048 | 51.937046 |
| 97.5% | 103.933588 | 95.583254 | 97.323221 | 118.441945 | 132.925730 | 89.602914 | 64.740581 | 75.239969 | 207.276980 | 199.118649 | 176.967152 | 151.489954 | 82.306710 | 70.843367 | 82.599720 | 115.133447 | 101.320813 | 85.090230 | 117.149755 | 126.313890 | 86.609769 | 64.681776 | 62.560937 | 88.473722 | 82.241410 | 71.411044 | 68.937072 | 102.916788 | 63.650568 | 68.051423 | 73.901229 | 152.518865 | 139.632716 | 118.621598 | 152.911916 | 143.660622 | 106.731261 | 86.165339 | 81.044174 | 131.539968 | 115.334738 | 85.048185 | 94.474197 | 122.003194 | 91.682238 | 79.805848 | 67.431143 | 65.458598 | 58.714801 | 57.496046 |
| max | 116.851289 | 106.351969 | 120.540637 | 126.315515 | 143.808370 | 111.083014 | 67.441523 | 82.950998 | 426.367353 | 778.858911 | 295.869659 | 292.514372 | 104.810763 | 128.198562 | 104.163372 | 117.537375 | 105.779767 | 87.800296 | 124.496771 | 127.472113 | 101.922449 | 66.022374 | 64.060605 | 113.244225 | 89.751867 | 74.783118 | 78.324788 | 114.593187 | 71.453293 | 73.002232 | 82.061544 | 162.037417 | 145.127694 | 122.350396 | 156.028937 | 144.684377 | 109.363575 | 91.640258 | 86.033215 | 174.184442 | 152.744892 | 137.166419 | 122.917740 | 193.165455 | 132.634029 | 120.496917 | 87.835021 | 68.143543 | 62.088274 | 63.711292 |
Revenue_hist_optcf_f(rt)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 97.383341 | 81.933103 | 80.399598 | 98.337395 | 101.255145 | 69.287040 | 54.830548 | 53.883991 | 149.192436 | 80.347571 | 79.848675 | 84.395666 | 55.213807 | 56.717095 | 58.282512 | NaN | 94.943911 | 75.989413 | 106.772965 | 110.967069 | 76.995658 | 57.049205 | 57.084254 | 73.260644 | 67.043250 | 59.917161 | 59.637142 | 73.590786 | 54.056863 | 55.260440 | 59.939343 | 113.628707 | 102.967354 | 82.285210 | 103.588846 | 103.571142 | 73.856640 | 60.016645 | 54.867060 | 100.114408 | 93.644976 | 73.500016 | 75.547609 | 91.632134 | 66.804731 | 58.730709 | 58.175067 | 44.451403 | 40.501281 | 39.456444 |
| std | 10.789130 | 7.673545 | 13.325080 | 6.537813 | 9.140125 | 10.637636 | 4.960570 | 7.212112 | 13.388035 | 44.873751 | 19.277648 | 14.512568 | 4.987191 | 5.904052 | 8.911151 | NaN | 4.537097 | 3.888703 | 7.163677 | 5.116133 | 2.570858 | 2.926616 | 3.077326 | 7.402907 | 7.063501 | 5.861208 | 6.579268 | 10.656896 | 6.660088 | 6.611914 | 8.587664 | 29.692648 | 24.522276 | 17.577749 | 32.072987 | 21.040395 | 15.916859 | 15.165319 | 12.147695 | 21.153357 | 15.321099 | 6.778444 | 9.007610 | 13.667067 | 9.047200 | 7.661092 | 4.619127 | 3.071947 | 7.284882 | 7.350931 |
| min | 51.070740 | 59.563879 | 45.561790 | 80.509229 | 79.236077 | 42.841082 | 33.778017 | 27.826508 | 129.058755 | 62.932544 | 66.772353 | 72.717154 | 47.472676 | 48.831916 | 35.322076 | NaN | 79.852823 | 65.503191 | 83.752796 | 94.736047 | 68.085368 | 46.584139 | 36.514438 | 52.146149 | 50.671697 | 40.998470 | 37.805957 | 34.903865 | 37.677918 | 34.943804 | 41.593879 | 73.566140 | 67.309719 | 58.707083 | 56.933855 | 54.156004 | 36.163124 | 31.323744 | 30.013405 | 46.328597 | 63.415392 | 59.622172 | 60.144651 | 59.167099 | 42.067349 | 37.751920 | 45.906879 | 35.683326 | 24.534421 | 16.487856 |
| 2.5% | 79.962447 | 65.525861 | 58.694705 | 85.801711 | 88.097041 | 51.183557 | 45.594587 | 40.140121 | 135.169008 | 64.812043 | 68.982385 | 74.547617 | 50.434933 | 52.015167 | 49.336312 | NaN | 86.420153 | 69.091122 | 92.218752 | 100.464073 | 70.150784 | 48.937462 | 46.663027 | 58.478083 | 53.508832 | 48.782820 | 46.243968 | 55.202733 | 42.083453 | 40.656617 | 46.746613 | 74.615701 | 70.260115 | 60.910039 | 59.264636 | 69.096340 | 46.027522 | 32.970313 | 31.845105 | 75.009021 | 73.525858 | 65.161414 | 64.485819 | 74.265051 | 56.345561 | 48.696140 | 50.873202 | 36.744722 | 27.140630 | 30.513905 |
| 25% | 90.474501 | 77.541906 | 71.777296 | 94.168444 | 95.138763 | 59.903849 | 51.726545 | 49.279463 | 143.930722 | 68.742494 | 72.929978 | 78.893075 | 52.787009 | 54.072354 | 52.738248 | NaN | 91.616091 | 73.153077 | 101.608385 | 108.167476 | 75.569785 | 55.679191 | 56.374634 | 69.660212 | 62.376573 | 55.761479 | 56.535742 | 67.862806 | 50.118637 | 51.272673 | 54.003958 | 81.252400 | 77.330389 | 65.855771 | 72.862473 | 82.694814 | 59.375192 | 48.145915 | 44.711936 | 80.953705 | 79.554292 | 69.155799 | 67.411408 | 80.750080 | 61.330469 | 54.681888 | 54.906461 | 42.654918 | 35.620734 | 35.269917 |
| 50% | 94.208201 | 80.671016 | 76.621147 | 98.699543 | 99.847372 | 70.537787 | 55.657016 | 52.814589 | 147.202962 | 70.834097 | 74.592091 | 81.070067 | 53.928449 | 55.293798 | 55.402269 | NaN | 95.315083 | 75.994444 | 108.962743 | 111.806277 | 77.476387 | 57.768955 | 57.717520 | 74.620116 | 67.368666 | 59.939321 | 60.310346 | 72.871556 | 54.091967 | 54.724383 | 57.560303 | 117.453727 | 102.822357 | 81.261827 | 103.703793 | 108.451109 | 76.588783 | 59.509091 | 56.736561 | 90.926518 | 90.472253 | 71.671996 | 73.717128 | 87.549573 | 64.386584 | 57.117897 | 57.307566 | 45.203972 | 40.173297 | 37.422478 |
| 75% | 105.650250 | 87.906132 | 87.807050 | 103.679805 | 104.931149 | 76.438079 | 58.533210 | 58.849791 | 151.751025 | 72.567124 | 78.258475 | 83.782106 | 55.470331 | 57.398486 | 60.692260 | NaN | 98.591732 | 78.148675 | 112.369067 | 114.754629 | 78.879280 | 59.179083 | 58.692769 | 77.112808 | 70.709486 | 63.739195 | 63.406566 | 78.008657 | 57.209916 | 59.418730 | 65.620191 | 141.323122 | 128.564433 | 94.276219 | 125.067757 | 117.235535 | 87.736983 | 73.771412 | 64.923206 | 120.202757 | 107.706333 | 76.492220 | 82.725102 | 101.219987 | 71.138116 | 62.008205 | 60.854280 | 46.234724 | 44.769346 | 41.721657 |
| 97.5% | 117.711876 | 97.468346 | 110.509515 | 108.335215 | 125.277196 | 91.561252 | 62.807131 | 69.971012 | 175.099512 | 193.964647 | 153.284960 | 134.583828 | 70.543404 | 68.099911 | 82.103967 | NaN | 102.214150 | 87.524096 | 115.765264 | 119.509831 | 80.906720 | 60.887302 | 60.655079 | 86.084977 | 82.078583 | 70.693996 | 72.496147 | 102.388870 | 69.023536 | 68.542420 | 78.149964 | 160.141739 | 151.733667 | 123.329648 | 182.228015 | 154.481630 | 102.206167 | 88.307539 | 72.066345 | 134.491301 | 120.000765 | 87.376019 | 92.890206 | 117.639111 | 89.641035 | 78.742475 | 66.709339 | 49.831682 | 54.432988 | 56.307908 |
| max | 130.018708 | 99.718389 | 120.537862 | 115.165879 | 132.967096 | 110.014158 | 64.641734 | 74.620503 | 326.267108 | 694.822127 | 219.394761 | 197.115729 | 87.213937 | 141.959905 | 128.990140 | NaN | 108.772527 | 91.392987 | 121.951244 | 120.849700 | 83.699451 | 62.485980 | 62.265714 | 114.945681 | 87.924704 | 82.178646 | 79.830470 | 114.276118 | 70.988370 | 72.925825 | 86.511683 | 160.141739 | 153.641869 | 123.838466 | 204.811975 | 161.456743 | 105.433792 | 98.416609 | 77.479656 | 164.998095 | 142.928318 | 138.588866 | 125.289742 | 187.543788 | 128.250100 | 111.255032 | 80.494622 | 52.701057 | 58.409003 | 60.736481 |
ValueAverage_hist_optcf_f(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 45.175477 | 40.237222 | 36.303819 | 47.481951 | 50.994203 | 34.050025 | 27.128030 | 27.708089 | 79.275421 | 43.978884 | 42.241667 | 46.988858 | 36.076308 | 30.178078 | 30.631365 | 62.701305 | 57.080122 | 43.348422 | 62.898107 | 70.691908 | 46.726974 | 33.267879 | 34.606131 | 43.852098 | 41.093687 | 33.099851 | 35.500079 | 43.574573 | 30.953006 | 30.460430 | 32.883802 | 66.534707 | 62.509990 | 47.897464 | 60.056870 | 64.322450 | 43.538906 | 33.636858 | 34.244757 | 57.241275 | 55.410852 | 40.478193 | 44.752154 | 54.405264 | 40.520470 | 33.531293 | 33.451273 | 30.790701 | 24.539064 | 24.305935 |
| std | 1.737670 | 1.901685 | 3.342362 | 2.845445 | 3.864041 | 3.895063 | 2.071822 | 3.342419 | 5.134065 | 16.197393 | 9.472674 | 6.762032 | 1.618363 | 2.503227 | 4.007041 | 2.558203 | 1.662777 | 1.401016 | 1.623996 | 1.745539 | 1.598961 | 0.858068 | 1.091594 | 4.723929 | 5.063609 | 4.158174 | 4.082144 | 5.693991 | 3.200513 | 3.127798 | 3.816320 | 12.151837 | 12.236261 | 8.864698 | 13.502036 | 9.572891 | 7.944598 | 6.599698 | 6.104059 | 9.343655 | 6.604753 | 3.270237 | 3.914005 | 6.981187 | 4.819349 | 3.801254 | 2.141938 | 1.142863 | 1.227478 | 2.260371 |
| min | 33.313455 | 28.391637 | 29.209682 | 41.114637 | 42.012615 | 25.643363 | 18.246635 | 12.993617 | 60.899811 | 36.437815 | 35.801061 | 40.005112 | 33.776167 | 26.829091 | 21.251504 | 55.011258 | 52.674335 | 40.901957 | 58.110155 | 64.704386 | 43.091132 | 29.473429 | 25.390258 | 33.593195 | 28.939186 | 23.871001 | 25.677652 | 26.549879 | 21.980653 | 21.042636 | 23.417048 | 47.526796 | 44.566424 | 36.441812 | 38.790390 | 39.838320 | 26.843099 | 20.533519 | 21.521889 | 33.961543 | 38.811916 | 30.034810 | 33.039955 | 36.890734 | 24.813719 | 22.010187 | 27.745947 | 27.527130 | 21.816118 | 20.040742 |
| 2.5% | 41.852121 | 35.982966 | 31.426159 | 42.523440 | 45.338306 | 28.324880 | 22.622647 | 20.772181 | 72.730362 | 39.290432 | 38.860819 | 44.198561 | 34.667877 | 28.212429 | 26.756687 | 57.172491 | 53.600850 | 41.352215 | 58.821619 | 66.606596 | 43.804245 | 31.342216 | 31.511263 | 34.678524 | 29.720089 | 25.084395 | 26.858685 | 34.291704 | 24.681864 | 23.642875 | 26.157327 | 48.546923 | 45.653807 | 37.131438 | 40.261450 | 47.023664 | 29.728164 | 20.964718 | 22.045894 | 44.438845 | 45.299737 | 36.167194 | 39.905608 | 45.778179 | 34.451172 | 28.125044 | 29.741175 | 29.204024 | 22.774140 | 21.489551 |
| 25% | 44.349947 | 39.338735 | 34.138560 | 45.388035 | 48.706638 | 31.444474 | 25.799738 | 25.543496 | 78.034765 | 40.154849 | 39.305126 | 44.922410 | 35.081047 | 29.116750 | 28.318281 | 60.875639 | 55.755952 | 42.607818 | 62.089156 | 70.037166 | 45.943473 | 32.859338 | 34.481204 | 41.402486 | 38.543199 | 30.291891 | 32.139662 | 39.847108 | 28.624818 | 28.051891 | 30.474410 | 53.792882 | 50.284977 | 40.391129 | 46.352870 | 53.856054 | 36.842006 | 28.839687 | 28.851087 | 48.170289 | 50.025771 | 38.252528 | 41.443186 | 48.659258 | 37.921311 | 32.062366 | 32.238860 | 29.941842 | 23.723351 | 22.266408 |
| 50% | 45.325221 | 40.417082 | 35.591168 | 47.221461 | 50.624526 | 33.848354 | 27.562865 | 27.703784 | 79.033268 | 40.427457 | 39.773405 | 45.230950 | 35.470877 | 29.540111 | 29.010892 | 62.599238 | 56.953720 | 43.116905 | 63.002813 | 70.751555 | 46.708785 | 33.328241 | 34.789771 | 43.293228 | 41.132766 | 33.426375 | 36.261442 | 43.106767 | 31.837814 | 30.620093 | 32.132992 | 66.853404 | 61.773388 | 46.395278 | 60.355995 | 68.232393 | 45.406957 | 33.805654 | 35.720484 | 54.546457 | 54.993100 | 40.112320 | 44.514600 | 52.933508 | 39.430827 | 33.158103 | 33.341268 | 30.262247 | 24.467022 | 24.026835 |
| 75% | 46.172183 | 41.439087 | 37.220480 | 49.535416 | 52.423757 | 35.830691 | 28.664876 | 29.722195 | 80.056644 | 41.362006 | 41.296605 | 46.365705 | 36.755853 | 30.359646 | 31.481967 | 64.956181 | 58.375755 | 43.891171 | 63.799581 | 71.642525 | 47.233286 | 33.658519 | 35.030785 | 46.459535 | 45.294587 | 35.552686 | 38.354987 | 46.081817 | 33.204016 | 32.753133 | 35.540907 | 76.797627 | 75.044306 | 54.769807 | 73.577152 | 69.636073 | 48.373132 | 37.519940 | 39.442471 | 65.464140 | 61.176059 | 42.287993 | 47.043122 | 58.898181 | 42.455278 | 34.978738 | 34.818651 | 31.677827 | 25.325330 | 25.506782 |
| 97.5% | 47.974505 | 43.070405 | 44.445137 | 53.333409 | 61.901275 | 42.628439 | 30.027871 | 34.382648 | 90.463622 | 86.185197 | 75.644844 | 67.829329 | 39.217138 | 34.887011 | 42.383883 | 66.306422 | 59.685034 | 49.125540 | 65.588970 | 74.614059 | 49.104235 | 34.940479 | 35.830433 | 51.033141 | 47.881166 | 41.380995 | 42.344131 | 58.083430 | 36.204449 | 35.633473 | 40.042180 | 86.088328 | 80.487189 | 66.039380 | 84.777650 | 81.816996 | 60.573986 | 45.744940 | 45.881320 | 73.154238 | 65.827356 | 46.631942 | 54.911353 | 69.185571 | 52.771575 | 43.211627 | 37.085119 | 32.948971 | 26.934637 | 28.577872 |
| max | 51.138572 | 46.800609 | 54.646675 | 54.949088 | 65.550925 | 51.850500 | 33.016643 | 40.043755 | 180.349037 | 336.886631 | 128.588504 | 137.372822 | 49.667198 | 65.213393 | 52.523915 | 68.874514 | 61.840130 | 49.998567 | 69.726218 | 75.338511 | 56.960907 | 35.491711 | 37.047420 | 70.606498 | 56.492077 | 47.209960 | 46.239649 | 64.177441 | 39.863414 | 39.262600 | 43.651335 | 91.530305 | 83.694936 | 66.694580 | 86.942269 | 84.260185 | 61.837104 | 52.523487 | 47.783136 | 90.246885 | 80.060863 | 71.803614 | 67.635080 | 104.543246 | 72.657013 | 62.346818 | 47.560762 | 35.123601 | 28.823508 | 32.996176 |
ValueAverage_hist_optcf_f(rt)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 47.722470 | 38.134409 | 37.446310 | 43.541327 | 46.956412 | 31.951283 | 25.312546 | 25.040576 | 71.740774 | 38.692348 | 39.547605 | 42.837528 | 29.068410 | 28.177017 | 29.195855 | NaN | 57.386932 | 43.748047 | 62.382588 | 66.191907 | 43.950614 | 31.321595 | 32.941855 | 42.428204 | 40.199509 | 32.955593 | 35.080771 | 41.631802 | 30.607480 | 30.000587 | 33.011493 | 67.730433 | 62.393870 | 48.218626 | 61.475648 | 62.660579 | 42.712367 | 33.351253 | 32.097764 | 57.335540 | 56.011095 | 41.247943 | 44.579776 | 53.977953 | 38.903443 | 32.836291 | 33.341369 | 22.946866 | 19.382700 | 18.983464 |
| std | 3.596859 | 2.811932 | 5.967216 | 3.046055 | 4.445673 | 5.409374 | 2.661176 | 3.514949 | 4.915115 | 18.765068 | 7.240725 | 5.910189 | 1.807479 | 3.125806 | 4.735743 | NaN | 1.780798 | 1.710803 | 2.028008 | 1.644101 | 1.059716 | 0.896241 | 1.355520 | 4.457444 | 4.644408 | 3.896611 | 4.356412 | 5.886771 | 3.790689 | 3.379628 | 4.318758 | 13.467296 | 11.431600 | 8.555320 | 15.756086 | 9.894016 | 8.115088 | 7.447802 | 5.922334 | 10.109035 | 7.050657 | 3.153037 | 4.659081 | 6.453600 | 4.735337 | 3.784676 | 2.068701 | 1.481363 | 2.207097 | 3.002755 |
| min | 25.656461 | 28.169699 | 26.050272 | 35.050690 | 36.409243 | 20.373873 | 14.917904 | 13.054663 | 61.666777 | 30.100248 | 34.652613 | 38.758187 | 26.106244 | 24.033717 | 15.524729 | NaN | 52.621013 | 40.226043 | 56.292143 | 60.382968 | 40.433814 | 28.096233 | 20.804917 | 33.139887 | 28.273194 | 22.007783 | 21.237074 | 21.111324 | 20.956010 | 20.228213 | 21.919904 | 47.654999 | 45.205316 | 36.129485 | 37.071455 | 37.196611 | 21.911577 | 18.304269 | 18.583957 | 28.849814 | 38.416382 | 32.385147 | 33.670349 | 34.514497 | 23.846809 | 21.634801 | 27.615963 | 18.415059 | 13.351143 | 8.307456 |
| 2.5% | 42.211340 | 30.901124 | 29.585655 | 37.694997 | 39.833722 | 22.282715 | 19.947675 | 18.265009 | 65.244088 | 33.172320 | 35.885133 | 39.976350 | 27.407564 | 25.625441 | 24.536075 | NaN | 53.724158 | 41.125231 | 57.056777 | 62.512804 | 41.626862 | 28.765631 | 28.618913 | 34.394593 | 30.194533 | 26.177683 | 25.658877 | 31.605877 | 23.158561 | 22.975925 | 26.198811 | 49.003968 | 46.721866 | 37.285034 | 37.793285 | 45.320932 | 27.897653 | 19.123702 | 19.205457 | 44.156959 | 44.901593 | 36.993894 | 38.811331 | 45.200059 | 33.358857 | 26.960790 | 29.321262 | 18.831548 | 15.640684 | 14.315065 |
| 25% | 45.321445 | 36.592467 | 33.463885 | 41.484507 | 44.123682 | 27.438398 | 24.064748 | 22.333303 | 69.764634 | 34.186375 | 36.751761 | 40.787187 | 28.045733 | 26.966500 | 26.483221 | NaN | 56.252230 | 42.760607 | 61.429011 | 65.337710 | 43.374614 | 30.990880 | 32.667344 | 39.808056 | 37.217245 | 29.778996 | 32.310780 | 38.856896 | 27.918146 | 27.687678 | 30.060792 | 53.986528 | 51.579024 | 41.027799 | 47.789293 | 53.819205 | 36.518490 | 28.125172 | 27.598300 | 47.751831 | 50.078733 | 39.171773 | 40.540526 | 49.336735 | 35.968213 | 31.318231 | 32.263453 | 22.400026 | 17.719566 | 16.302861 |
| 50% | 46.945908 | 37.881238 | 35.546166 | 43.754006 | 47.074163 | 33.193210 | 25.910069 | 25.011672 | 71.076056 | 34.519891 | 37.518670 | 41.357884 | 28.553451 | 27.542636 | 27.621548 | NaN | 57.200568 | 43.572244 | 62.770983 | 66.180022 | 44.117595 | 31.430099 | 33.125203 | 41.735509 | 39.981424 | 33.230227 | 35.717012 | 41.062071 | 30.718069 | 29.891158 | 32.137731 | 67.335810 | 62.023122 | 47.427459 | 61.095318 | 66.128568 | 43.297521 | 32.537043 | 33.042092 | 53.880279 | 56.037231 | 40.858529 | 44.325946 | 53.510857 | 37.790873 | 32.603484 | 33.385809 | 23.187285 | 19.214541 | 19.410569 |
| 75% | 50.039086 | 39.797043 | 40.595816 | 46.074434 | 48.857096 | 35.233354 | 27.247834 | 27.819860 | 72.951362 | 35.240706 | 39.186716 | 42.802924 | 29.498469 | 28.307955 | 30.482552 | NaN | 58.563235 | 44.265220 | 63.753554 | 67.254875 | 44.647653 | 31.853389 | 33.549094 | 44.937974 | 44.127721 | 35.264507 | 37.682468 | 43.880060 | 32.767407 | 32.310833 | 35.918673 | 80.122308 | 74.320041 | 53.893217 | 71.180449 | 68.526742 | 49.640432 | 39.780411 | 36.559460 | 66.113581 | 62.014214 | 42.830309 | 47.240138 | 57.769963 | 41.249711 | 34.501592 | 34.691266 | 24.051309 | 20.592927 | 20.734315 |
| 97.5% | 55.642947 | 44.220885 | 50.444815 | 48.792808 | 58.346928 | 41.658164 | 28.627464 | 31.960051 | 79.821264 | 86.487046 | 67.099756 | 59.544351 | 34.354405 | 33.870302 | 42.669838 | NaN | 61.230793 | 50.535786 | 65.283695 | 69.252369 | 45.833692 | 32.691725 | 34.498770 | 49.908430 | 48.043689 | 40.205422 | 44.314321 | 57.770145 | 38.491947 | 36.741395 | 42.263403 | 88.484402 | 86.594131 | 68.382460 | 103.261302 | 87.979731 | 57.082776 | 47.721725 | 41.424692 | 75.754769 | 67.785406 | 47.885189 | 55.353135 | 66.885117 | 51.498965 | 42.391175 | 36.746131 | 24.892340 | 23.537740 | 25.795319 |
| max | 56.869385 | 44.866865 | 54.572420 | 51.113866 | 61.328208 | 50.599382 | 33.178101 | 35.075066 | 138.007656 | 300.006977 | 95.318513 | 89.325593 | 48.368216 | 72.213658 | 63.937091 | NaN | 61.772892 | 51.676310 | 68.044938 | 70.148506 | 48.159494 | 33.983111 | 35.346495 | 71.667337 | 55.342015 | 51.878695 | 46.308729 | 63.999868 | 40.116250 | 39.221507 | 44.407004 | 89.056471 | 87.563832 | 68.664584 | 114.125099 | 91.952168 | 63.448290 | 56.407343 | 43.032461 | 85.487338 | 74.915530 | 72.548234 | 68.940266 | 101.926090 | 70.404004 | 57.564935 | 47.245435 | 25.880763 | 26.436307 | 28.116618 |
ValueFactor_hist_optcf_f(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.110444 | 1.193349 | 1.148942 | 1.064677 | 1.021081 | 0.977549 | 0.903693 | 0.801583 | 1.597733 | 1.403138 | 1.203839 | 1.151099 | 1.350195 | 1.322542 | 1.249446 | 1.138182 | 1.126206 | 1.118057 | 1.075804 | 1.071918 | 1.090439 | 1.100763 | 1.050301 | 1.220296 | 1.220006 | 1.186538 | 1.146395 | 1.110429 | 1.155971 | 1.179214 | 1.177995 | 1.142858 | 1.152084 | 1.164686 | 1.130840 | 1.068750 | 1.150780 | 1.181556 | 1.120997 | 1.176081 | 1.214755 | 1.168228 | 1.157981 | 1.088965 | 1.166595 | 1.171990 | 1.140107 | 0.972401 | 0.873984 | 0.765088 |
| std | 0.014350 | 0.015394 | 0.041868 | 0.022447 | 0.021162 | 0.050178 | 0.044231 | 0.062013 | 0.037601 | 0.084605 | 0.085153 | 0.055240 | 0.024463 | 0.042867 | 0.054614 | 0.007917 | 0.010699 | 0.011466 | 0.010408 | 0.023345 | 0.022383 | 0.007103 | 0.012271 | 0.025432 | 0.033600 | 0.042726 | 0.034594 | 0.046720 | 0.035481 | 0.029733 | 0.036575 | 0.024720 | 0.035165 | 0.048053 | 0.028136 | 0.034749 | 0.032840 | 0.077645 | 0.044109 | 0.040093 | 0.035213 | 0.018591 | 0.019657 | 0.026325 | 0.028020 | 0.022072 | 0.032445 | 0.035397 | 0.039732 | 0.063360 |
| min | 1.056745 | 1.097519 | 1.035543 | 1.003922 | 0.923115 | 0.806810 | 0.700139 | 0.451965 | 1.323257 | 1.163643 | 1.110578 | 0.948250 | 1.222508 | 1.241004 | 0.860535 | 1.119896 | 1.082202 | 1.091326 | 1.031159 | 0.966255 | 0.984190 | 1.079167 | 1.007036 | 1.171007 | 1.154172 | 1.093460 | 1.047737 | 0.973476 | 0.981508 | 1.120423 | 1.111534 | 1.099664 | 1.103470 | 1.101220 | 1.065151 | 0.884865 | 1.016456 | 1.074447 | 1.041994 | 0.989044 | 1.092808 | 1.122762 | 1.106164 | 0.961911 | 1.062234 | 1.117059 | 0.965958 | 0.883659 | 0.794868 | 0.647222 |
| 2.5% | 1.083454 | 1.163646 | 1.095132 | 1.031357 | 0.980434 | 0.879919 | 0.793333 | 0.671348 | 1.531973 | 1.345093 | 1.154867 | 1.118458 | 1.319398 | 1.286726 | 1.192033 | 1.122241 | 1.103681 | 1.105307 | 1.051325 | 1.025123 | 1.054573 | 1.088616 | 1.030922 | 1.177151 | 1.170519 | 1.120714 | 1.103441 | 1.053315 | 1.107577 | 1.133737 | 1.129260 | 1.104611 | 1.105804 | 1.108591 | 1.089349 | 0.986165 | 1.089643 | 1.089912 | 1.061121 | 1.120089 | 1.154070 | 1.140766 | 1.128262 | 1.044027 | 1.115953 | 1.135154 | 1.092015 | 0.922238 | 0.816979 | 0.693864 |
| 25% | 1.100363 | 1.184714 | 1.121083 | 1.049349 | 1.004867 | 0.935731 | 0.871896 | 0.761790 | 1.576816 | 1.365590 | 1.169742 | 1.132721 | 1.334518 | 1.302276 | 1.212436 | 1.132650 | 1.118994 | 1.110817 | 1.072540 | 1.061366 | 1.077824 | 1.097062 | 1.043044 | 1.200330 | 1.194969 | 1.160643 | 1.125080 | 1.079152 | 1.134398 | 1.155939 | 1.151939 | 1.119568 | 1.118197 | 1.142229 | 1.107144 | 1.059591 | 1.134572 | 1.143814 | 1.101077 | 1.151975 | 1.193487 | 1.155760 | 1.144717 | 1.072390 | 1.143606 | 1.157676 | 1.119698 | 0.945730 | 0.849933 | 0.714267 |
| 50% | 1.109605 | 1.193562 | 1.139462 | 1.059504 | 1.022801 | 0.993296 | 0.919551 | 0.816433 | 1.591313 | 1.376841 | 1.182064 | 1.137982 | 1.343341 | 1.311012 | 1.232272 | 1.138015 | 1.128503 | 1.115548 | 1.077531 | 1.069726 | 1.087579 | 1.099718 | 1.048331 | 1.219608 | 1.214730 | 1.175683 | 1.137146 | 1.101950 | 1.146485 | 1.173928 | 1.173494 | 1.152851 | 1.150326 | 1.150527 | 1.133439 | 1.082699 | 1.152415 | 1.157107 | 1.118062 | 1.165117 | 1.207836 | 1.165354 | 1.153043 | 1.086669 | 1.164359 | 1.169964 | 1.137926 | 0.955955 | 0.872516 | 0.754621 |
| 75% | 1.121073 | 1.201464 | 1.159033 | 1.072330 | 1.033891 | 1.007882 | 0.938259 | 0.843112 | 1.615690 | 1.401108 | 1.201482 | 1.151009 | 1.365563 | 1.328559 | 1.271679 | 1.144296 | 1.134562 | 1.123743 | 1.081940 | 1.081777 | 1.102349 | 1.103239 | 1.055324 | 1.235362 | 1.235564 | 1.205976 | 1.157770 | 1.133950 | 1.174128 | 1.200828 | 1.191547 | 1.164764 | 1.183222 | 1.157951 | 1.142880 | 1.086780 | 1.162718 | 1.200988 | 1.132290 | 1.187071 | 1.231634 | 1.176903 | 1.167761 | 1.104086 | 1.192991 | 1.187087 | 1.153789 | 1.007043 | 0.888963 | 0.790495 |
| 97.5% | 1.134649 | 1.232153 | 1.255920 | 1.118123 | 1.062420 | 1.061294 | 0.957645 | 0.915864 | 1.684966 | 1.709042 | 1.532025 | 1.294104 | 1.396932 | 1.429653 | 1.389966 | 1.152741 | 1.141510 | 1.163878 | 1.091152 | 1.123353 | 1.131609 | 1.118150 | 1.076808 | 1.274763 | 1.293309 | 1.282107 | 1.221074 | 1.216974 | 1.236026 | 1.237403 | 1.268782 | 1.185650 | 1.195983 | 1.262294 | 1.186220 | 1.109411 | 1.248784 | 1.443335 | 1.256380 | 1.249406 | 1.289521 | 1.212022 | 1.197563 | 1.143066 | 1.217042 | 1.214015 | 1.202961 | 1.035620 | 0.961668 | 0.899100 |
| max | 1.139584 | 1.253313 | 1.367523 | 1.131657 | 1.075579 | 1.085500 | 1.000511 | 0.976529 | 1.779045 | 1.940491 | 1.813841 | 1.664182 | 1.534029 | 1.727105 | 1.507225 | 1.153784 | 1.147259 | 1.173878 | 1.130427 | 1.153818 | 1.202821 | 1.131670 | 1.207329 | 1.304819 | 1.301418 | 1.326116 | 1.253033 | 1.249215 | 1.244111 | 1.266791 | 1.310353 | 1.198685 | 1.263254 | 1.341058 | 1.191331 | 1.131118 | 1.305214 | 1.626424 | 1.405340 | 1.357076 | 1.368157 | 1.272452 | 1.282994 | 1.167482 | 1.302858 | 1.300367 | 1.286612 | 1.045935 | 0.980711 | 0.940674 |
ValueFactor_hist_optcf_f(rt)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.076385 | 1.128881 | 1.163639 | 1.035682 | 0.979687 | 0.935351 | 0.843889 | 0.788523 | 1.540910 | 1.331508 | 1.183320 | 1.098544 | 1.203592 | 1.291599 | 1.211012 | NaN | 1.129131 | 1.131662 | 1.074999 | 1.031408 | 1.053954 | 1.072517 | 0.996258 | 1.230620 | 1.223286 | 1.201184 | 1.141932 | 1.078776 | 1.161812 | 1.181211 | 1.196760 | 1.149758 | 1.176439 | 1.191559 | 1.164109 | 1.091420 | 1.138383 | 1.170128 | 1.069274 | 1.178286 | 1.229746 | 1.181455 | 1.174780 | 1.106187 | 1.140445 | 1.170188 | 1.142499 | 1.071532 | 0.854701 | 0.765804 |
| std | 0.039554 | 0.037368 | 0.078174 | 0.033217 | 0.041908 | 0.097212 | 0.064592 | 0.079220 | 0.048391 | 0.121905 | 0.085964 | 0.045604 | 0.035983 | 0.059359 | 0.080243 | NaN | 0.010387 | 0.016744 | 0.012499 | 0.028346 | 0.021660 | 0.014107 | 0.031355 | 0.041639 | 0.049303 | 0.050387 | 0.035957 | 0.049684 | 0.046957 | 0.042347 | 0.050140 | 0.033788 | 0.029593 | 0.038740 | 0.027715 | 0.043266 | 0.059533 | 0.091498 | 0.059612 | 0.042762 | 0.030648 | 0.026855 | 0.040830 | 0.034515 | 0.032433 | 0.030664 | 0.047447 | 0.027538 | 0.077395 | 0.149520 |
| min | 0.883010 | 1.012611 | 0.965454 | 0.932919 | 0.827715 | 0.677990 | 0.576613 | 0.469798 | 1.362208 | 1.193384 | 1.042890 | 0.963705 | 1.131437 | 1.191316 | 0.541563 | NaN | 1.100954 | 1.084208 | 1.028125 | 0.919583 | 0.956089 | 1.047963 | 0.941391 | 1.150118 | 1.137031 | 1.099574 | 1.028934 | 0.976499 | 0.978954 | 1.106253 | 1.034639 | 1.076050 | 1.129086 | 1.126580 | 1.082293 | 0.869195 | 0.951044 | 1.056039 | 0.983216 | 0.872169 | 1.090315 | 1.131937 | 1.084954 | 0.980578 | 1.026705 | 1.071764 | 0.873785 | 0.933781 | 0.696110 | 0.545964 |
| 2.5% | 1.028627 | 1.063988 | 1.050626 | 0.950516 | 0.885077 | 0.728756 | 0.703206 | 0.639226 | 1.468502 | 1.250695 | 1.113680 | 1.058282 | 1.165971 | 1.243550 | 1.137975 | NaN | 1.114080 | 1.112532 | 1.047879 | 0.983499 | 1.011337 | 1.052224 | 0.970608 | 1.172068 | 1.158796 | 1.126294 | 1.090868 | 1.007407 | 1.096754 | 1.121498 | 1.123872 | 1.089418 | 1.132348 | 1.139359 | 1.110559 | 0.991331 | 1.037231 | 1.064117 | 1.004091 | 1.108823 | 1.162935 | 1.143651 | 1.130053 | 1.042859 | 1.076479 | 1.120523 | 1.058271 | 1.012965 | 0.742472 | 0.579391 |
| 25% | 1.047898 | 1.107970 | 1.110163 | 1.012383 | 0.950848 | 0.850840 | 0.798293 | 0.728410 | 1.506037 | 1.275370 | 1.143319 | 1.080248 | 1.182944 | 1.265507 | 1.160394 | NaN | 1.120877 | 1.121657 | 1.065544 | 1.014415 | 1.037846 | 1.064172 | 0.981646 | 1.194158 | 1.190454 | 1.169766 | 1.120056 | 1.038193 | 1.132202 | 1.148093 | 1.162683 | 1.122972 | 1.158284 | 1.168706 | 1.151521 | 1.069719 | 1.113955 | 1.125329 | 1.035448 | 1.143946 | 1.211526 | 1.162376 | 1.151843 | 1.079436 | 1.117918 | 1.145908 | 1.116828 | 1.058510 | 0.809748 | 0.625515 |
| 50% | 1.062646 | 1.120612 | 1.145248 | 1.041000 | 0.992912 | 0.976788 | 0.870209 | 0.811701 | 1.533552 | 1.290854 | 1.160513 | 1.087337 | 1.196270 | 1.276788 | 1.184371 | NaN | 1.126617 | 1.128399 | 1.077664 | 1.027523 | 1.054393 | 1.071098 | 0.988141 | 1.222089 | 1.212205 | 1.190303 | 1.135742 | 1.069602 | 1.156216 | 1.177482 | 1.191962 | 1.144497 | 1.177433 | 1.184282 | 1.166363 | 1.110393 | 1.129825 | 1.164329 | 1.051551 | 1.177101 | 1.224964 | 1.177129 | 1.164494 | 1.110727 | 1.144031 | 1.165288 | 1.144193 | 1.069051 | 0.850056 | 0.741065 |
| 75% | 1.097451 | 1.151650 | 1.186980 | 1.059612 | 1.006352 | 1.009649 | 0.890129 | 0.851000 | 1.567627 | 1.325862 | 1.188607 | 1.101589 | 1.212809 | 1.294037 | 1.241396 | NaN | 1.136410 | 1.137501 | 1.085202 | 1.046358 | 1.069807 | 1.076375 | 0.998443 | 1.267007 | 1.243710 | 1.221510 | 1.156235 | 1.112969 | 1.185323 | 1.204782 | 1.227363 | 1.183361 | 1.200878 | 1.208718 | 1.175521 | 1.119498 | 1.151370 | 1.184590 | 1.081391 | 1.206069 | 1.254168 | 1.190039 | 1.183776 | 1.135929 | 1.161939 | 1.197736 | 1.165733 | 1.087674 | 0.890366 | 0.845970 |
| 97.5% | 1.157595 | 1.229773 | 1.347417 | 1.083893 | 1.059011 | 1.072257 | 0.927579 | 0.905468 | 1.641696 | 1.760667 | 1.501106 | 1.231143 | 1.310437 | 1.410076 | 1.447983 | NaN | 1.150811 | 1.200920 | 1.092205 | 1.097526 | 1.093585 | 1.103004 | 1.090158 | 1.314256 | 1.326719 | 1.318667 | 1.221843 | 1.195860 | 1.277927 | 1.276642 | 1.317364 | 1.201752 | 1.223421 | 1.272329 | 1.239269 | 1.139793 | 1.335012 | 1.503750 | 1.261377 | 1.253093 | 1.288304 | 1.239452 | 1.326460 | 1.158043 | 1.198401 | 1.224913 | 1.228505 | 1.125369 | 1.007720 | 1.033176 |
| max | 1.179287 | 1.237704 | 1.404304 | 1.122203 | 1.079433 | 1.114281 | 0.971997 | 0.937086 | 1.792053 | 2.121927 | 1.701891 | 1.532755 | 1.528232 | 1.796781 | 1.620960 | NaN | 1.155443 | 1.213145 | 1.096285 | 1.128290 | 1.109420 | 1.214950 | 1.482201 | 1.370531 | 1.381830 | 1.361444 | 1.282900 | 1.252162 | 1.328237 | 1.365266 | 1.371905 | 1.210511 | 1.289410 | 1.310590 | 1.274053 | 1.173316 | 1.516640 | 1.703000 | 1.447970 | 1.333850 | 1.360069 | 1.285923 | 1.364994 | 1.314645 | 1.457411 | 1.290563 | 1.287909 | 1.142447 | 1.045332 | 1.073048 |
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
'CF_curtail/CF_mustrun(fixed)(da)',
'CF_curtail/CF_mustrun(fixed)(rt)',
'Rev_curtail/Rev_mustrun(fixed)(da)',
'Rev_curtail/Rev_mustrun(fixed)(rt)',
]
colindex = [0, 0, 1, 1]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))
### Column-indexed parameters
ncols = 2
ylim = [
[0.68, 1.02],
[0.98, 1.32],
]
ylabel = [
'Capacity Factor',
'Revenue',
]
note = [
'',
'',
]
y1 = 1 # 1.2 if using note
y2 = 1.04 # 1.07 if using note
gridspec_kw = {'width_ratios': [2, 2]}
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey=False, gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
# format_axes=False,
)
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
ax[(row,col)].set_xlim(2009.4,2018)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.1))
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(-1,0)].legend(
handles=patches, loc='lower left', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, Curtailable vs. Must-run, fixed', weight='bold', y=y2, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
print(datum)
display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
| CF_curtail/CF_mustrun(fixed)(da) | CF_curtail/CF_mustrun(fixed)(rt) | Rev_curtail/Rev_mustrun(fixed)(da) | Rev_curtail/Rev_mustrun(fixed)(rt) | |
|---|---|---|---|---|
| count | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 |
| mean | 0.946114 | 0.839164 | 1.012793 | 1.116364 |
| std | 0.025391 | 0.028136 | 0.025054 | 0.088171 |
| min | 0.734967 | 0.704510 | 1.003795 | 1.058217 |
| 2.5% | 0.873700 | 0.755800 | 1.005002 | 1.062509 |
| 25% | 0.945421 | 0.829134 | 1.006094 | 1.071480 |
| 50% | 0.954375 | 0.844459 | 1.007082 | 1.090087 |
| 75% | 0.959726 | 0.860207 | 1.008928 | 1.123006 |
| 97.5% | 0.963765 | 0.867057 | 1.054005 | 1.305881 |
| max | 0.968056 | 0.879552 | 1.486920 | 2.011486 |
median
| CF_curtail/CF_mustrun(fixed)(da) | CF_curtail/CF_mustrun(fixed)(rt) | Rev_curtail/Rev_mustrun(fixed)(da) | Rev_curtail/Rev_mustrun(fixed)(rt) | |||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | ||||||||||||||||||||||||||||
| 2010 | 0.999891 | NaN | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.992574 | NaN | NaN | 0.994818 | 0.998674 | 0.999160 | NaN | 1.000005 | NaN | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 1.003201 | NaN | NaN | 1.002599 | 1.002117 | 1.000501 | NaN |
| 2011 | 0.999336 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.954748 | 0.999052 | 0.999084 | 0.994701 | 0.999010 | 0.999012 | NaN | 1.000028 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 1.008916 | 1.000475 | 1.000000 | 1.003016 | 1.000875 | 1.000101 | NaN |
| 2012 | 1.000000 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.993981 | 0.999194 | 0.999549 | 0.996336 | 0.999521 | 0.999780 | NaN | 1.000000 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 1.007790 | 1.000890 | 1.000000 | 1.002905 | 1.000702 | 1.000071 | NaN |
| 2013 | 1.000000 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.988362 | 0.999825 | 0.999294 | 0.996476 | 0.999359 | 1.000000 | NaN | 1.000000 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 1.011310 | 1.000137 | 1.000000 | 1.002096 | 1.000941 | 1.000000 | NaN |
| 2014 | 0.996845 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.968861 | 0.999016 | 0.989827 | 0.997497 | 0.999057 | 0.998801 | NaN | 1.000016 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 1.016974 | 1.000153 | 1.000098 | 1.001488 | 1.000601 | 1.000516 | NaN |
| 2015 | 0.999576 | 1.0 | 0.999983 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.950535 | 0.997184 | 0.997776 | 0.997206 | 0.998593 | 0.998381 | 0.956260 | 1.000039 | 1.0 | 1.000002 | 1.0 | 1.0 | 1.0 | 1.000000 | 1.026694 | 1.000627 | 1.001910 | 1.001491 | 1.001117 | 1.001004 | 1.062874 |
| 2016 | 0.994956 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | 0.999761 | 0.892457 | 0.998501 | 0.991553 | 0.997408 | 0.997897 | 0.999278 | 0.900596 | 1.000723 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | 1.000001 | 1.063702 | 1.000980 | 1.010817 | 1.002235 | 1.004416 | 1.000335 | 1.088473 |
| 2017 | 0.954375 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | 0.944457 | 0.844459 | 0.997431 | 0.995044 | 0.997846 | 0.998177 | 0.999005 | 0.846251 | 1.007082 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | 1.007793 | 1.090087 | 1.001346 | 1.003300 | 1.001176 | 1.001133 | 1.000261 | 1.161483 |
max
| CF_curtail/CF_mustrun(fixed)(da) | CF_curtail/CF_mustrun(fixed)(rt) | Rev_curtail/Rev_mustrun(fixed)(da) | Rev_curtail/Rev_mustrun(fixed)(rt) | |||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | ||||||||||||||||||||||||||||
| 2010 | 0.999967 | NaN | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.995393 | NaN | NaN | 0.999929 | 0.999217 | 0.999989 | NaN | 1.007511 | NaN | 1.000381 | 1.032036 | 1.000083 | 1.088616 | NaN | 1.283891 | NaN | NaN | 1.089373 | 1.032796 | 1.459818 | NaN |
| 2011 | 0.999751 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.962186 | 0.999963 | 0.999673 | 0.999722 | 0.999722 | 0.999872 | NaN | 1.017282 | 1.181707 | 1.000089 | 1.006524 | 1.000000 | 1.056131 | NaN | 1.129260 | 1.115124 | 1.016475 | 1.065589 | 1.014703 | 1.112005 | NaN |
| 2012 | 1.000000 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.996781 | 0.999995 | 0.999770 | 1.000000 | 0.999899 | 0.999985 | NaN | 1.062973 | 1.441546 | 1.001437 | 1.104133 | 1.000915 | 1.040329 | NaN | 1.244553 | 1.109393 | 1.016019 | 1.369107 | 1.028196 | 1.107201 | NaN |
| 2013 | 1.000000 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.992584 | 1.000000 | 0.999662 | 1.000000 | 0.999894 | 1.000000 | NaN | 1.011328 | 1.020913 | 1.003430 | 1.105501 | 1.003456 | 1.031728 | NaN | 1.075805 | 1.094200 | 1.001646 | 1.465850 | 1.061580 | 1.068820 | NaN |
| 2014 | 1.000000 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | NaN | 0.975334 | 0.999933 | 0.994899 | 1.000000 | 0.999388 | 0.999968 | NaN | 1.019664 | 1.063308 | 1.000731 | 1.065033 | 1.000000 | 1.029761 | NaN | 1.155153 | 1.055808 | 1.003568 | 1.419973 | 1.053398 | 1.150497 | NaN |
| 2015 | 1.000000 | 1.0 | 0.999989 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.958292 | 0.999841 | 0.998326 | 1.000000 | 0.999299 | 0.999973 | 0.966608 | 1.038759 | 1.024715 | 1.006559 | 1.072204 | 1.002804 | 1.139016 | 1.000179 | 1.304408 | 1.065644 | 1.004631 | 1.331792 | 1.084671 | 1.264873 | 1.144309 |
| 2016 | 1.000000 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.912507 | 0.999521 | 0.993714 | 1.000000 | 0.999218 | 0.999996 | 0.922624 | 1.059724 | 1.061083 | 1.021643 | 1.023589 | 1.001834 | 1.033304 | 1.009883 | 1.396916 | 1.111239 | 1.064501 | 1.119620 | 1.260819 | 1.154599 | 1.333018 |
| 2017 | 0.968056 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 | 0.964118 | 0.879552 | 0.999973 | 0.996733 | 1.000000 | 0.999243 | 0.999991 | 0.917047 | 1.486920 | 1.072424 | 1.190638 | 1.036037 | 1.001351 | 1.015338 | 1.033989 | 2.011486 | 1.344622 | 1.461626 | 1.244391 | 1.213275 | 1.091430 | 2.012393 |
CF_curtail/CF_mustrun(fixed)(da)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.0 | 424.000000 | 430.000000 | 434.0 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 0.999801 | 0.998804 | 0.999370 | 0.999726 | 0.996999 | 0.997345 | 0.992236 | 0.946114 | 0.997992 | 0.999090 | 0.999804 | 0.999692 | 0.999823 | 0.999529 | 0.998931 | 0.999974 | 0.999990 | 0.999948 | 0.999975 | 0.999959 | 0.999947 | 0.999912 | 0.999363 | 0.999387 | 0.999531 | 0.997503 | 0.997525 | 0.998416 | 0.998493 | 0.999312 | 0.999245 | 0.999993 | 1.0 | 0.999987 | 0.999778 | 1.0 | 0.999943 | 0.999908 | 0.999966 | 0.999617 | 0.999901 | 0.999702 | 0.999877 | 0.999837 | 0.999740 | 0.999781 | 0.999849 | 0.999988 | 0.998109 | 0.940377 |
| std | 0.000511 | 0.003582 | 0.003594 | 0.001185 | 0.001484 | 0.003702 | 0.009445 | 0.025391 | 0.006467 | 0.007907 | 0.001499 | 0.002042 | 0.001033 | 0.002064 | 0.004179 | 0.000198 | 0.000142 | 0.000498 | 0.000146 | 0.000265 | 0.000355 | 0.000757 | 0.005176 | 0.003683 | 0.001620 | 0.011433 | 0.010022 | 0.007519 | 0.005545 | 0.003129 | 0.003622 | 0.000081 | 0.0 | 0.000154 | 0.001583 | 0.0 | 0.000504 | 0.000574 | 0.000343 | 0.003466 | 0.001412 | 0.002565 | 0.001759 | 0.001062 | 0.002272 | 0.001973 | 0.001152 | 0.000093 | 0.003126 | 0.013541 |
| min | 0.994518 | 0.926922 | 0.947499 | 0.988563 | 0.980110 | 0.954078 | 0.920260 | 0.734967 | 0.854365 | 0.725999 | 0.970030 | 0.958049 | 0.985618 | 0.961378 | 0.920014 | 0.997526 | 0.997901 | 0.991583 | 0.998082 | 0.997303 | 0.992127 | 0.983212 | 0.893201 | 0.954895 | 0.985832 | 0.873062 | 0.903590 | 0.876523 | 0.950844 | 0.954013 | 0.945890 | 0.999055 | 1.0 | 0.998168 | 0.982580 | 1.0 | 0.992872 | 0.994425 | 0.995167 | 0.915006 | 0.952195 | 0.929750 | 0.954900 | 0.975433 | 0.937845 | 0.958886 | 0.978373 | 0.999101 | 0.987920 | 0.887235 |
| 2.5% | 0.998756 | 0.994915 | 0.994438 | 0.994905 | 0.995411 | 0.990164 | 0.972038 | 0.873700 | 0.982063 | 0.992659 | 0.998448 | 0.997504 | 0.997905 | 0.994869 | 0.991996 | 1.000000 | 1.000000 | 0.999519 | 0.999372 | 0.999439 | 0.999774 | 0.999222 | 0.997508 | 0.993915 | 0.994171 | 0.982243 | 0.985092 | 0.990624 | 0.984293 | 0.992985 | 0.994990 | 1.000000 | 1.0 | 1.000000 | 0.996282 | 1.0 | 0.999994 | 0.998539 | 1.000000 | 0.997823 | 0.999525 | 0.997965 | 0.999936 | 0.997812 | 0.998171 | 0.998342 | 0.998936 | 1.000000 | 0.991894 | 0.924118 |
| 25% | 0.999857 | 0.999225 | 1.000000 | 1.000000 | 0.996732 | 0.995141 | 0.987382 | 0.945421 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.999961 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.999979 | 1.000000 | 1.000000 | 1.000000 | 0.999990 | 0.999189 | 0.999401 | 0.999653 | 0.999876 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.998871 | 0.927142 |
| 50% | 0.999891 | 0.999336 | 1.000000 | 1.000000 | 0.996845 | 0.999576 | 0.994956 | 0.954375 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.999983 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.999761 | 0.944457 |
| 75% | 0.999918 | 0.999552 | 1.000000 | 1.000000 | 0.997110 | 1.000000 | 1.000000 | 0.959726 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.999985 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.950900 |
| 97.5% | 0.999956 | 0.999676 | 1.000000 | 1.000000 | 0.999497 | 1.000000 | 1.000000 | 0.963765 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.999987 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.958160 |
| max | 0.999967 | 0.999751 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.968056 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.999989 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.964118 |
CF_curtail/CF_mustrun(fixed)(rt)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 0.992355 | 0.952957 | 0.992018 | 0.987062 | 0.963292 | 0.920131 | 0.875298 | 0.839164 | 0.986608 | 0.992268 | 0.997905 | 0.998359 | 0.996696 | 0.994842 | 0.990580 | NaN | 0.999054 | 0.999460 | 0.999085 | 0.990215 | 0.997744 | 0.991003 | 0.991827 | 0.991375 | 0.989409 | 0.990109 | 0.988523 | 0.993012 | 0.993079 | 0.994000 | 0.995170 | 0.997564 | 0.998627 | 0.998951 | 0.996754 | 0.997361 | 0.994824 | 0.989016 | 0.991905 | 0.996896 | 0.997467 | 0.998516 | 0.999284 | 0.997900 | 0.996624 | 0.997759 | 0.998232 | 0.955709 | 0.890160 | 0.845761 |
| std | 0.009986 | 0.007156 | 0.004195 | 0.006190 | 0.009657 | 0.040977 | 0.031010 | 0.028136 | 0.033568 | 0.019451 | 0.004836 | 0.003140 | 0.002676 | 0.007921 | 0.017115 | NaN | 0.000437 | 0.000526 | 0.001466 | 0.001185 | 0.000413 | 0.003110 | 0.011210 | 0.010055 | 0.013950 | 0.017380 | 0.021710 | 0.015424 | 0.011220 | 0.008866 | 0.007534 | 0.001939 | 0.001604 | 0.001615 | 0.010943 | 0.005872 | 0.009497 | 0.017054 | 0.014515 | 0.005607 | 0.003634 | 0.005336 | 0.002582 | 0.003297 | 0.006902 | 0.005329 | 0.002853 | 0.005161 | 0.021461 | 0.034932 |
| min | 0.762575 | 0.910830 | 0.961229 | 0.953063 | 0.917312 | 0.825962 | 0.741424 | 0.704510 | 0.845957 | 0.803607 | 0.939958 | 0.954757 | 0.977393 | 0.950507 | 0.708194 | NaN | 0.996023 | 0.989842 | 0.985119 | 0.987323 | 0.994792 | 0.963586 | 0.902317 | 0.954254 | 0.918662 | 0.837002 | 0.821874 | 0.799943 | 0.912106 | 0.924050 | 0.941941 | 0.991223 | 0.988063 | 0.988991 | 0.927052 | 0.958637 | 0.948170 | 0.927682 | 0.928524 | 0.914363 | 0.961644 | 0.925965 | 0.954423 | 0.940864 | 0.907735 | 0.933168 | 0.968333 | 0.931374 | 0.846265 | 0.729627 |
| 2.5% | 0.989753 | 0.929307 | 0.982634 | 0.969009 | 0.940646 | 0.844756 | 0.818729 | 0.755800 | 0.879823 | 0.930233 | 0.984200 | 0.993137 | 0.991756 | 0.972728 | 0.950395 | NaN | 0.997809 | 0.998420 | 0.997898 | 0.988325 | 0.996644 | 0.982210 | 0.957609 | 0.963912 | 0.948932 | 0.950177 | 0.918648 | 0.964714 | 0.958203 | 0.970717 | 0.971678 | 0.991645 | 0.993705 | 0.992185 | 0.939498 | 0.969798 | 0.952034 | 0.935243 | 0.933548 | 0.976356 | 0.987003 | 0.989693 | 0.994945 | 0.990003 | 0.982694 | 0.988690 | 0.988392 | 0.943406 | 0.851520 | 0.790500 |
| 25% | 0.991982 | 0.951287 | 0.988919 | 0.985803 | 0.957079 | 0.875198 | 0.842222 | 0.829134 | 0.997920 | 0.996519 | 0.998651 | 0.998540 | 0.995315 | 0.995820 | 0.992877 | NaN | 0.998945 | 0.999481 | 0.999228 | 0.989619 | 0.997648 | 0.991201 | 0.994317 | 0.987855 | 0.988634 | 0.990898 | 0.989331 | 0.993995 | 0.992088 | 0.994065 | 0.994075 | 0.996628 | 0.998454 | 0.999080 | 0.998141 | 0.997826 | 0.995242 | 0.988822 | 0.990889 | 0.995880 | 0.996701 | 0.999083 | 0.999494 | 0.997275 | 0.995852 | 0.997468 | 0.997891 | 0.952065 | 0.878129 | 0.813131 |
| 50% | 0.992574 | 0.954748 | 0.993981 | 0.988362 | 0.968861 | 0.950535 | 0.892457 | 0.844459 | 0.999052 | 0.999194 | 0.999825 | 0.999016 | 0.997184 | 0.998501 | 0.997431 | NaN | 0.999084 | 0.999549 | 0.999294 | 0.989827 | 0.997776 | 0.991553 | 0.995044 | 0.994818 | 0.994701 | 0.996336 | 0.996476 | 0.997497 | 0.997206 | 0.997408 | 0.997846 | 0.998674 | 0.999010 | 0.999521 | 0.999359 | 0.999057 | 0.998593 | 0.997897 | 0.998177 | 0.999160 | 0.999012 | 0.999780 | 1.000000 | 0.998801 | 0.998381 | 0.999278 | 0.999005 | 0.956260 | 0.900596 | 0.846251 |
| 75% | 0.993928 | 0.957670 | 0.995049 | 0.990578 | 0.970922 | 0.954159 | 0.902456 | 0.860207 | 0.999652 | 0.999869 | 0.999951 | 0.999287 | 0.998847 | 0.998974 | 0.998942 | NaN | 0.999331 | 0.999646 | 0.999466 | 0.990368 | 0.997941 | 0.992251 | 0.995230 | 0.998810 | 0.997331 | 0.998968 | 0.999407 | 0.998834 | 0.999239 | 0.999363 | 0.999600 | 0.998975 | 0.999673 | 0.999683 | 0.999787 | 0.999309 | 0.999064 | 0.998769 | 0.999055 | 0.999793 | 0.999264 | 0.999880 | 1.000000 | 0.999607 | 0.999741 | 0.999903 | 0.999891 | 0.960038 | 0.905443 | 0.874680 |
| 97.5% | 0.995014 | 0.961060 | 0.996112 | 0.992126 | 0.972243 | 0.957265 | 0.908433 | 0.867057 | 0.999934 | 0.999988 | 0.999995 | 0.999591 | 0.999752 | 0.999384 | 0.999884 | NaN | 0.999470 | 0.999743 | 0.999587 | 0.992944 | 0.998188 | 0.993237 | 0.996142 | 0.999892 | 0.999578 | 0.999778 | 0.999979 | 0.999980 | 1.000000 | 1.000000 | 1.000000 | 0.999212 | 0.999722 | 0.999839 | 0.999854 | 0.999388 | 0.999143 | 0.999059 | 0.999169 | 0.999930 | 0.999733 | 0.999955 | 1.000000 | 0.999957 | 0.999922 | 0.999972 | 0.999971 | 0.963017 | 0.912190 | 0.900861 |
| max | 0.995393 | 0.962186 | 0.996781 | 0.992584 | 0.975334 | 0.958292 | 0.912507 | 0.879552 | 0.999963 | 0.999995 | 1.000000 | 0.999933 | 0.999841 | 0.999521 | 0.999973 | NaN | 0.999673 | 0.999770 | 0.999662 | 0.994899 | 0.998326 | 0.993714 | 0.996733 | 0.999929 | 0.999722 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.999217 | 0.999722 | 0.999899 | 0.999894 | 0.999388 | 0.999299 | 0.999218 | 0.999243 | 0.999989 | 0.999872 | 0.999985 | 1.000000 | 0.999968 | 0.999973 | 0.999996 | 0.999991 | 0.966608 | 0.922624 | 0.917047 |
Rev_curtail/Rev_mustrun(fixed)(da)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.0 | 424.000000 | 430.000000 | 434.0 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.000073 | 1.000480 | 1.000599 | 1.000094 | 1.000156 | 1.000790 | 1.001747 | 1.012793 | 1.000648 | 1.000522 | 1.000100 | 1.000367 | 1.000110 | 1.000582 | 1.000623 | 1.000006 | 1.000000 | 1.000009 | 1.000011 | 1.000005 | 1.000024 | 1.000029 | 1.000275 | 1.000299 | 1.000099 | 1.001640 | 1.001969 | 1.000838 | 1.000996 | 1.000335 | 1.000535 | 1.000001 | 1.0 | 1.000006 | 1.000068 | 1.0 | 1.000017 | 1.000013 | 1.000008 | 1.000222 | 1.000067 | 1.000130 | 1.000070 | 1.000091 | 1.000172 | 1.000098 | 1.000072 | 1.000002 | 1.000273 | 1.009808 |
| std | 0.000463 | 0.001704 | 0.003897 | 0.000569 | 0.001055 | 0.003141 | 0.004442 | 0.025054 | 0.007241 | 0.011310 | 0.000834 | 0.003050 | 0.000985 | 0.003703 | 0.003378 | 0.000045 | 0.000004 | 0.000079 | 0.000151 | 0.000054 | 0.000324 | 0.000752 | 0.006161 | 0.002449 | 0.000530 | 0.010325 | 0.011180 | 0.004104 | 0.006070 | 0.001961 | 0.002913 | 0.000007 | 0.0 | 0.000077 | 0.000414 | 0.0 | 0.000181 | 0.000142 | 0.000095 | 0.003163 | 0.001505 | 0.001492 | 0.000956 | 0.000794 | 0.002586 | 0.000939 | 0.000689 | 0.000014 | 0.000628 | 0.004427 |
| min | 1.000000 | 1.000009 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.003795 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000001 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.004525 |
| 2.5% | 1.000002 | 1.000013 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.005002 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000001 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.005443 |
| 25% | 1.000004 | 1.000019 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.006094 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000002 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.006652 |
| 50% | 1.000005 | 1.000028 | 1.000000 | 1.000000 | 1.000016 | 1.000039 | 1.000723 | 1.007082 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000002 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000001 | 1.007793 |
| 75% | 1.000007 | 1.000044 | 1.000000 | 1.000000 | 1.000021 | 1.000625 | 1.001461 | 1.008928 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000001 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000002 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000039 | 1.000074 | 1.000067 | 1.000008 | 1.000000 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000020 | 1.013532 |
| 97.5% | 1.000782 | 1.007509 | 1.003848 | 1.000906 | 1.000922 | 1.004448 | 1.012152 | 1.054005 | 1.002237 | 1.001611 | 1.001175 | 1.003106 | 1.000730 | 1.006612 | 1.007219 | 1.000000 | 1.000000 | 1.000028 | 1.000005 | 1.000005 | 1.000004 | 1.000000 | 1.000315 | 1.002445 | 1.001253 | 1.005218 | 1.011240 | 1.009122 | 1.006600 | 1.002129 | 1.006186 | 1.000000 | 1.0 | 1.000000 | 1.001313 | 1.0 | 1.000000 | 1.000005 | 1.000000 | 1.000520 | 1.000070 | 1.000538 | 1.000011 | 1.000733 | 1.000581 | 1.000489 | 1.000354 | 1.000000 | 1.001060 | 1.014892 |
| max | 1.007511 | 1.017282 | 1.062973 | 1.011328 | 1.019664 | 1.038759 | 1.059724 | 1.486920 | 1.181707 | 1.441546 | 1.020913 | 1.063308 | 1.024715 | 1.061083 | 1.072424 | 1.000381 | 1.000089 | 1.001437 | 1.003430 | 1.000731 | 1.006559 | 1.021643 | 1.190638 | 1.032036 | 1.006524 | 1.104133 | 1.105501 | 1.065033 | 1.072204 | 1.023589 | 1.036037 | 1.000083 | 1.0 | 1.000915 | 1.003456 | 1.0 | 1.002804 | 1.001834 | 1.001351 | 1.088616 | 1.056131 | 1.040329 | 1.031728 | 1.029761 | 1.139016 | 1.033304 | 1.015338 | 1.000179 | 1.009883 | 1.033989 |
Rev_curtail/Rev_mustrun(fixed)(rt)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.004650 | 1.014679 | 1.018781 | 1.014150 | 1.027885 | 1.081936 | 1.081495 | 1.116364 | 1.004388 | 1.003656 | 1.001417 | 1.001397 | 1.002758 | 1.003635 | 1.007032 | NaN | 1.000203 | 1.000122 | 1.000041 | 1.000191 | 1.001919 | 1.012073 | 1.007956 | 1.007112 | 1.006992 | 1.012386 | 1.014515 | 1.008891 | 1.008607 | 1.007034 | 1.006288 | 1.004739 | 1.001682 | 1.001970 | 1.003391 | 1.002043 | 1.003628 | 1.018964 | 1.013387 | 1.002564 | 1.001150 | 1.001030 | 1.000635 | 1.001896 | 1.003505 | 1.002271 | 1.002405 | 1.066430 | 1.105749 | 1.212859 |
| std | 0.012589 | 0.017010 | 0.027827 | 0.010857 | 0.016902 | 0.074476 | 0.038637 | 0.088171 | 0.009921 | 0.008257 | 0.004874 | 0.004587 | 0.006477 | 0.007230 | 0.017579 | NaN | 0.001448 | 0.000818 | 0.000140 | 0.000309 | 0.000328 | 0.005884 | 0.022555 | 0.011872 | 0.010813 | 0.034750 | 0.052513 | 0.031862 | 0.029981 | 0.014490 | 0.019655 | 0.004616 | 0.002118 | 0.004431 | 0.009714 | 0.005144 | 0.009011 | 0.047474 | 0.039700 | 0.011388 | 0.004385 | 0.005312 | 0.003330 | 0.006482 | 0.010221 | 0.008473 | 0.004666 | 0.013734 | 0.042090 | 0.150963 |
| min | 1.001490 | 1.005180 | 1.003763 | 1.004397 | 1.012153 | 1.012642 | 1.040916 | 1.058217 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000004 | 1.000001 | NaN | 1.000000 | 1.000000 | 1.000000 | 1.000013 | 1.001295 | 1.006703 | 1.002079 | 1.000044 | 1.000041 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000983 | 1.000267 | 1.000113 | 1.000023 | 1.000261 | 1.000370 | 1.000398 | 1.000282 | 1.000001 | 1.000006 | 1.000005 | 1.000000 | 1.000000 | 1.000030 | 1.000001 | 1.000000 | 1.045542 | 1.052763 | 1.034115 |
| 2.5% | 1.001740 | 1.005644 | 1.005006 | 1.004943 | 1.013085 | 1.015725 | 1.045254 | 1.062509 | 1.000006 | 1.000015 | 1.000000 | 1.000003 | 1.000005 | 1.000028 | 1.000046 | NaN | 1.000000 | 1.000000 | 1.000000 | 1.000026 | 1.001470 | 1.007252 | 1.002489 | 1.000061 | 1.000053 | 1.000048 | 1.000001 | 1.000004 | 1.000000 | 1.000000 | 1.000000 | 1.000983 | 1.000267 | 1.000197 | 1.000032 | 1.000261 | 1.000388 | 1.000398 | 1.000282 | 1.000003 | 1.000017 | 1.000015 | 1.000000 | 1.000000 | 1.000143 | 1.000010 | 1.000004 | 1.049361 | 1.058312 | 1.051075 |
| 25% | 1.002439 | 1.007019 | 1.006209 | 1.007427 | 1.015084 | 1.021240 | 1.051116 | 1.071480 | 1.000102 | 1.000263 | 1.000046 | 1.000058 | 1.000160 | 1.000283 | 1.000655 | NaN | 1.000000 | 1.000000 | 1.000000 | 1.000060 | 1.001764 | 1.010001 | 1.003210 | 1.000318 | 1.001469 | 1.000769 | 1.000114 | 1.000583 | 1.000169 | 1.000353 | 1.000108 | 1.001290 | 1.000335 | 1.000280 | 1.000218 | 1.000299 | 1.000500 | 1.000525 | 1.000624 | 1.000014 | 1.000045 | 1.000034 | 1.000000 | 1.000022 | 1.000307 | 1.000045 | 1.000010 | 1.054289 | 1.072477 | 1.083456 |
| 50% | 1.003201 | 1.008916 | 1.007790 | 1.011310 | 1.016974 | 1.026694 | 1.063702 | 1.090087 | 1.000475 | 1.000890 | 1.000137 | 1.000153 | 1.000627 | 1.000980 | 1.001346 | NaN | 1.000000 | 1.000000 | 1.000000 | 1.000098 | 1.001910 | 1.010817 | 1.003300 | 1.002599 | 1.003016 | 1.002905 | 1.002096 | 1.001488 | 1.001491 | 1.002235 | 1.001176 | 1.002117 | 1.000875 | 1.000702 | 1.000941 | 1.000601 | 1.001117 | 1.004416 | 1.001133 | 1.000501 | 1.000101 | 1.000071 | 1.000000 | 1.000516 | 1.001004 | 1.000335 | 1.000261 | 1.062874 | 1.088473 | 1.161483 |
| 75% | 1.003556 | 1.015572 | 1.016607 | 1.019119 | 1.040257 | 1.153218 | 1.105764 | 1.123006 | 1.002596 | 1.002714 | 1.000885 | 1.000573 | 1.002280 | 1.004027 | 1.006375 | NaN | 1.000000 | 1.000000 | 1.000000 | 1.000150 | 1.002042 | 1.011512 | 1.003782 | 1.009851 | 1.007876 | 1.011915 | 1.006609 | 1.004169 | 1.005576 | 1.006883 | 1.004176 | 1.007782 | 1.001933 | 1.001544 | 1.002522 | 1.001371 | 1.002055 | 1.014347 | 1.006032 | 1.002333 | 1.001184 | 1.000233 | 1.000114 | 1.001325 | 1.002432 | 1.001312 | 1.004162 | 1.077649 | 1.127282 | 1.369467 |
| 97.5% | 1.017662 | 1.071168 | 1.107211 | 1.042849 | 1.066153 | 1.239133 | 1.168655 | 1.305881 | 1.028339 | 1.026781 | 1.012096 | 1.012382 | 1.020464 | 1.020922 | 1.040413 | NaN | 1.000812 | 1.000862 | 1.000513 | 1.000927 | 1.002466 | 1.028604 | 1.065609 | 1.031893 | 1.043540 | 1.077718 | 1.084180 | 1.084611 | 1.047479 | 1.054960 | 1.034565 | 1.014443 | 1.007800 | 1.022849 | 1.052338 | 1.023656 | 1.040612 | 1.224227 | 1.195182 | 1.017574 | 1.009860 | 1.011176 | 1.005327 | 1.012424 | 1.022062 | 1.016620 | 1.014445 | 1.094313 | 1.203843 | 1.473657 |
| max | 1.283891 | 1.129260 | 1.244553 | 1.075805 | 1.155153 | 1.304408 | 1.396916 | 2.011486 | 1.115124 | 1.109393 | 1.094200 | 1.055808 | 1.065644 | 1.111239 | 1.344622 | NaN | 1.016475 | 1.016019 | 1.001646 | 1.003568 | 1.004631 | 1.064501 | 1.461626 | 1.089373 | 1.065589 | 1.369107 | 1.465850 | 1.419973 | 1.331792 | 1.119620 | 1.244391 | 1.032796 | 1.014703 | 1.028196 | 1.061580 | 1.053398 | 1.084671 | 1.260819 | 1.213275 | 1.459818 | 1.112005 | 1.107201 | 1.068820 | 1.150497 | 1.264873 | 1.154599 | 1.091430 | 1.144309 | 1.333018 | 2.012393 |
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
'CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun',
'CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun',
'CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun',
'Revenue_track(def)/fixed(optcf)_hist,da,mustrun',
'Revenue_track(def)/fixed(optcf)_hist,rt,mustrun',
'Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun',
'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun',
]
colindex = [0, 1, 1, 2, 2, 3, 3]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))
### Column-indexed parameters
ncols = 4
ylim = [
[0.92, 1.7],
[0.92, 1.7],
[0.92, 1.7],
[0.92, 1.7],
]
ylabel = [
'Capacity Factor',
'Capacity Factor',
'Revenue',
'Revenue',
]
note = [
'(must-run)',
'(curtailable)',
'(must-run)',
'(curtailable)',
]
y1 = 1.2 # 1.2 if using note, 1 if not
y2 = 1.07 # 1.07 if using note, 1.05 if not
gridspec_kw = {'width_ratios': [1, 2, 2, 2]}
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
# medianmarker='_', mediansize=10, medianfacecolor='k'
)
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
ax[(row,col)].set_xlim(2009.4,2018)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.2))
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
ax[(row,col)].axhline(1, lw=0.25, c='0.5')
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(-1,-1)].legend(
handles=patches, loc='upper right', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, 1-ax track vs. fixed', weight='bold', y=y2, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
print(datum)
display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
| CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun | CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun | CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun | Revenue_track(def)/fixed(optcf)_hist,da,mustrun | Revenue_track(def)/fixed(optcf)_hist,rt,mustrun | Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun | Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun | |
|---|---|---|---|---|---|---|---|
| count | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 |
| mean | 1.190360 | 1.133019 | 1.006021 | 1.283981 | 1.314155 | 1.297633 | 1.447377 |
| std | 0.016639 | 0.032371 | 0.033595 | 0.037652 | 0.052336 | 0.051749 | 0.131262 |
| min | 1.113813 | 0.888389 | 0.856991 | 1.161767 | 1.171260 | 1.166622 | 1.260330 |
| 2.5% | 1.144030 | 1.040829 | 0.905791 | 1.194076 | 1.204142 | 1.201355 | 1.298872 |
| 25% | 1.184059 | 1.125284 | 0.995113 | 1.265940 | 1.281787 | 1.275987 | 1.369893 |
| 50% | 1.195275 | 1.144368 | 1.014365 | 1.291287 | 1.321106 | 1.301618 | 1.422102 |
| 75% | 1.200167 | 1.153055 | 1.029195 | 1.308802 | 1.346624 | 1.318441 | 1.489774 |
| 97.5% | 1.213245 | 1.164177 | 1.044061 | 1.341588 | 1.401838 | 1.377570 | 1.680171 |
| max | 1.231872 | 1.180118 | 1.061693 | 1.426787 | 1.541156 | 1.889847 | 2.660819 |
median
| CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun | CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun | CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun | Revenue_track(def)/fixed(optcf)_hist,da,mustrun | Revenue_track(def)/fixed(optcf)_hist,rt,mustrun | Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun | Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | |||||||||||||||||||||||||||||||||||||||||||||||||
| 2010 | 1.213636 | NaN | 1.133700 | 1.158801 | 1.141078 | 1.150299 | NaN | 1.211987 | NaN | 1.133700 | 1.158801 | 1.141078 | 1.149981 | NaN | 1.195654 | NaN | NaN | 1.150550 | 1.138907 | 1.146701 | NaN | 1.188600 | NaN | 1.124925 | 1.162706 | 1.140239 | 1.155738 | NaN | 1.177229 | NaN | NaN | 1.165641 | 1.147034 | 1.161721 | NaN | 1.188673 | NaN | 1.124925 | 1.162711 | 1.140239 | 1.155748 | NaN | 1.186409 | NaN | NaN | 1.171407 | 1.149936 | 1.164177 | NaN |
| 2011 | 1.184462 | 1.196981 | 1.107946 | 1.133107 | 1.114666 | 1.129048 | NaN | 1.180264 | 1.194612 | 1.107946 | 1.132935 | 1.114666 | 1.128915 | NaN | 1.104376 | 1.192087 | 1.106373 | 1.126589 | 1.113642 | 1.125715 | NaN | 1.179614 | 1.186917 | 1.101876 | 1.148362 | 1.121465 | 1.144011 | NaN | 1.155977 | 1.173355 | 1.105066 | 1.150188 | 1.123311 | 1.149328 | NaN | 1.180388 | 1.187117 | 1.101876 | 1.148452 | 1.121465 | 1.144040 | NaN | 1.178076 | 1.175964 | 1.105148 | 1.156612 | 1.126299 | 1.150336 | NaN |
| 2012 | 1.204967 | 1.181716 | 1.120651 | 1.161251 | 1.124788 | 1.152504 | NaN | 1.204051 | 1.180981 | 1.120549 | 1.161177 | 1.124788 | 1.151963 | NaN | 1.193566 | 1.177798 | 1.119231 | 1.154546 | 1.123634 | 1.150442 | NaN | 1.188976 | 1.181531 | 1.102166 | 1.166099 | 1.124168 | 1.150721 | NaN | 1.205031 | 1.171462 | 1.111289 | 1.159714 | 1.130362 | 1.155261 | NaN | 1.189432 | 1.181629 | 1.102182 | 1.166617 | 1.124168 | 1.150815 | NaN | 1.225690 | 1.173431 | 1.111668 | 1.169032 | 1.132100 | 1.155768 | NaN |
| 2013 | 1.179953 | 1.193319 | 1.127070 | 1.139756 | 1.137081 | 1.144513 | NaN | 1.179298 | 1.193181 | 1.127025 | 1.138335 | 1.136634 | 1.144482 | NaN | 1.164574 | 1.191959 | 1.125867 | 1.133703 | 1.135139 | 1.144014 | NaN | 1.184977 | 1.201803 | 1.058814 | 1.145342 | 1.108983 | 1.154610 | NaN | 1.184263 | 1.196311 | 1.072903 | 1.150395 | 1.126810 | 1.156055 | NaN | 1.184996 | 1.201955 | 1.058814 | 1.145752 | 1.108983 | 1.154622 | NaN | 1.197539 | 1.197066 | 1.072903 | 1.154342 | 1.130936 | 1.156580 | NaN |
| 2014 | 1.198067 | 1.199874 | 1.137230 | 1.161745 | 1.143429 | 1.148970 | NaN | 1.194885 | 1.199732 | 1.137230 | 1.159924 | 1.143429 | 1.148854 | NaN | 1.159494 | 1.198316 | 1.122012 | 1.157926 | 1.141897 | 1.146129 | NaN | 1.213929 | 1.203752 | 1.041229 | 1.152205 | 1.065022 | 1.116371 | NaN | 1.233573 | 1.199399 | 1.044929 | 1.159114 | 1.055274 | 1.112866 | NaN | 1.214147 | 1.203867 | 1.041229 | 1.152388 | 1.065022 | 1.116469 | NaN | 1.257045 | 1.200431 | 1.044991 | 1.169862 | 1.060015 | 1.114013 | NaN |
| 2015 | 1.180203 | 1.184606 | 1.137664 | 1.155334 | 1.138507 | 1.143015 | 1.163519 | 1.178015 | 1.184385 | 1.137359 | 1.152007 | 1.138507 | 1.142780 | 1.163519 | 1.105928 | 1.181195 | 1.133806 | 1.150349 | 1.136527 | 1.139845 | 1.113950 | 1.213394 | 1.187814 | 1.081304 | 1.156076 | 1.104415 | 1.136464 | 1.206318 | 1.227655 | 1.185337 | 1.100862 | 1.156230 | 1.113386 | 1.136088 | 1.203407 | 1.214550 | 1.187864 | 1.081320 | 1.158087 | 1.104415 | 1.136532 | 1.206318 | 1.279313 | 1.187207 | 1.104563 | 1.161805 | 1.115801 | 1.139411 | 1.277179 |
| 2016 | 1.203699 | 1.185931 | 1.135127 | 1.163820 | 1.142993 | 1.149273 | 1.186228 | 1.193439 | 1.185682 | 1.135127 | 1.162919 | 1.142993 | 1.148988 | 1.182417 | 1.074811 | 1.183031 | 1.125233 | 1.157975 | 1.140578 | 1.147121 | 1.075006 | 1.248827 | 1.174446 | 1.109817 | 1.160812 | 1.129348 | 1.145575 | 1.247765 | 1.274670 | 1.172251 | 1.130612 | 1.163779 | 1.153420 | 1.149887 | 1.276794 | 1.250143 | 1.174605 | 1.109817 | 1.161288 | 1.129348 | 1.145645 | 1.247888 | 1.349841 | 1.174532 | 1.145583 | 1.169550 | 1.158917 | 1.151424 | 1.376338 |
| 2017 | 1.195275 | 1.188038 | 1.119281 | 1.169287 | 1.130560 | 1.150807 | 1.182490 | 1.144368 | 1.186584 | 1.119269 | 1.169110 | 1.130560 | 1.150511 | 1.121469 | 1.014365 | 1.179426 | 1.113376 | 1.165342 | 1.128395 | 1.148016 | 0.993127 | 1.291287 | 1.181682 | 1.076754 | 1.171774 | 1.120036 | 1.145315 | 1.299170 | 1.321106 | 1.182709 | 1.092580 | 1.169203 | 1.135918 | 1.143157 | 1.348387 | 1.301618 | 1.181987 | 1.076754 | 1.171930 | 1.120036 | 1.145348 | 1.308847 | 1.422102 | 1.185653 | 1.098862 | 1.172432 | 1.141490 | 1.145191 | 1.525901 |
max
| CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun | CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun | CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun | Revenue_track(def)/fixed(optcf)_hist,da,mustrun | Revenue_track(def)/fixed(optcf)_hist,rt,mustrun | Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun | Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | |||||||||||||||||||||||||||||||||||||||||||||||||
| 2010 | 1.245073 | NaN | 1.162213 | 1.180655 | 1.182804 | 1.200101 | NaN | 1.243720 | NaN | 1.162213 | 1.180655 | 1.182804 | 1.200101 | NaN | 1.226578 | NaN | NaN | 1.180655 | 1.179590 | 1.197883 | NaN | 1.218533 | NaN | 1.155768 | 1.186720 | 1.184335 | 1.198492 | NaN | 1.209193 | NaN | NaN | 1.191849 | 1.183515 | 1.200596 | NaN | 1.218605 | NaN | 1.155768 | 1.186720 | 1.184335 | 1.269284 | NaN | 1.423716 | NaN | NaN | 1.258766 | 1.193524 | 1.716022 | NaN |
| 2011 | 1.228367 | 1.230489 | 1.125571 | 1.164497 | 1.158220 | 1.156490 | NaN | 1.224192 | 1.230489 | 1.125571 | 1.164497 | 1.158220 | 1.156490 | NaN | 1.145995 | 1.230082 | 1.123325 | 1.162620 | 1.155850 | 1.155884 | NaN | 1.224095 | 1.230381 | 1.123286 | 1.181845 | 1.165063 | 1.167081 | NaN | 1.197676 | 1.271035 | 1.127995 | 1.176035 | 1.159652 | 1.169452 | NaN | 1.225300 | 1.413759 | 1.123286 | 1.181845 | 1.165063 | 1.202461 | NaN | 1.302028 | 1.392086 | 1.134053 | 1.215248 | 1.163234 | 1.280244 | NaN |
| 2012 | 1.238126 | 1.229003 | 1.150921 | 1.196338 | 1.185203 | 1.185883 | NaN | 1.238126 | 1.229003 | 1.150921 | 1.195794 | 1.185203 | 1.185883 | NaN | 1.230224 | 1.228479 | 1.149848 | 1.189540 | 1.184084 | 1.184563 | NaN | 1.243719 | 1.384686 | 1.129567 | 1.193939 | 1.173515 | 1.211865 | NaN | 1.413043 | 1.275370 | 1.136601 | 1.191777 | 1.172011 | 1.247257 | NaN | 1.266654 | 1.904386 | 1.129567 | 1.281184 | 1.173515 | 1.212101 | NaN | 1.544809 | 1.282667 | 1.136601 | 1.580827 | 1.181576 | 1.290533 | NaN |
| 2013 | 1.224838 | 1.219486 | 1.147316 | 1.191306 | 1.182401 | 1.180713 | NaN | 1.224838 | 1.219486 | 1.147316 | 1.186844 | 1.182401 | 1.180713 | NaN | 1.204673 | 1.218594 | 1.145687 | 1.186265 | 1.180544 | 1.180499 | NaN | 1.252418 | 1.223727 | 1.092672 | 1.188060 | 1.171687 | 1.180472 | NaN | 1.243834 | 1.230533 | 1.100520 | 1.230359 | 1.169610 | 1.188343 | NaN | 1.252573 | 1.225089 | 1.092672 | 1.279305 | 1.171687 | 1.194328 | NaN | 1.317285 | 1.299695 | 1.100520 | 1.678640 | 1.222366 | 1.243708 | NaN |
| 2014 | 1.233165 | 1.237561 | 1.161359 | 1.200816 | 1.168225 | 1.171282 | NaN | 1.230139 | 1.237561 | 1.161359 | 1.200327 | 1.168225 | 1.171282 | NaN | 1.192237 | 1.235745 | 1.147052 | 1.199350 | 1.165909 | 1.170763 | NaN | 1.261841 | 1.233995 | 1.078632 | 1.216253 | 1.140102 | 1.158249 | NaN | 1.300952 | 1.246152 | 1.087814 | 1.222688 | 1.134068 | 1.160664 | NaN | 1.261857 | 1.272803 | 1.078632 | 1.219495 | 1.140102 | 1.158249 | NaN | 1.418395 | 1.277193 | 1.087872 | 1.627221 | 1.175926 | 1.267371 | NaN |
| 2015 | 1.215767 | 1.212615 | 1.158233 | 1.189473 | 1.165617 | 1.167475 | 1.192918 | 1.213018 | 1.212615 | 1.158233 | 1.189473 | 1.165617 | 1.167475 | 1.192918 | 1.154679 | 1.209214 | 1.154194 | 1.188564 | 1.156939 | 1.166487 | 1.146181 | 1.338387 | 1.210145 | 1.104741 | 1.198028 | 1.153492 | 1.171540 | 1.250215 | 1.361951 | 1.241951 | 1.129040 | 1.236867 | 1.159474 | 1.181872 | 1.250999 | 1.338387 | 1.218542 | 1.104761 | 1.271597 | 1.153492 | 1.285252 | 1.250215 | 1.639772 | 1.269242 | 1.133987 | 1.574349 | 1.237520 | 1.435168 | 1.373498 |
| 2016 | 1.236797 | 1.213154 | 1.164883 | 1.199435 | 1.187438 | 1.185426 | 1.222750 | 1.226526 | 1.213154 | 1.164883 | 1.199435 | 1.187438 | 1.185426 | 1.222284 | 1.123683 | 1.210166 | 1.151005 | 1.198408 | 1.177256 | 1.185426 | 1.124463 | 1.355752 | 1.191988 | 1.141944 | 1.187579 | 1.191377 | 1.175216 | 1.300944 | 1.438653 | 1.198710 | 1.168823 | 1.195777 | 1.205303 | 1.195529 | 1.358766 | 1.407147 | 1.249642 | 1.162642 | 1.189986 | 1.193383 | 1.179436 | 1.300946 | 1.834946 | 1.310014 | 1.229645 | 1.324488 | 1.476765 | 1.342671 | 1.607390 |
| 2017 | 1.231872 | 1.225060 | 1.137418 | 1.194131 | 1.169199 | 1.183058 | 1.210316 | 1.180118 | 1.225060 | 1.137418 | 1.194131 | 1.169199 | 1.183058 | 1.158565 | 1.061693 | 1.217070 | 1.131445 | 1.194131 | 1.161518 | 1.182520 | 1.076046 | 1.426787 | 1.218830 | 1.109250 | 1.196287 | 1.155215 | 1.176106 | 1.346755 | 1.541156 | 1.242655 | 1.126912 | 1.199620 | 1.163344 | 1.185199 | 1.728387 | 1.889847 | 1.266207 | 1.274044 | 1.230165 | 1.155215 | 1.176106 | 1.379000 | 2.660819 | 1.584219 | 1.546495 | 1.464246 | 1.379264 | 1.240983 | 2.703056 |
CapacityFactor_track(def)/fixed(optcf)_hist,da,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.205674 | 1.182921 | 1.199947 | 1.179000 | 1.195210 | 1.178027 | 1.197063 | 1.190360 | 1.197914 | 1.183841 | 1.194293 | 1.199710 | 1.184308 | 1.186799 | 1.188641 | 1.136201 | 1.107736 | 1.122396 | 1.127026 | 1.134952 | 1.135156 | 1.135737 | 1.119598 | 1.156711 | 1.133030 | 1.156411 | 1.141178 | 1.160746 | 1.155958 | 1.162685 | 1.164538 | 1.147908 | 1.120182 | 1.137409 | 1.145147 | 1.144969 | 1.140727 | 1.152161 | 1.133892 | 1.156194 | 1.127473 | 1.147861 | 1.146251 | 1.149137 | 1.143271 | 1.149060 | 1.148573 | 1.161050 | 1.184067 | 1.176863 |
| std | 0.024422 | 0.023558 | 0.021722 | 0.019730 | 0.018567 | 0.020008 | 0.019118 | 0.016639 | 0.010385 | 0.009552 | 0.006582 | 0.009706 | 0.010552 | 0.007280 | 0.010146 | 0.008307 | 0.006472 | 0.007869 | 0.007290 | 0.009585 | 0.007628 | 0.006774 | 0.006926 | 0.013750 | 0.012781 | 0.014265 | 0.013471 | 0.023050 | 0.019664 | 0.014453 | 0.018491 | 0.015657 | 0.015037 | 0.020630 | 0.015148 | 0.007061 | 0.007513 | 0.015460 | 0.012040 | 0.015333 | 0.009066 | 0.015573 | 0.008908 | 0.008094 | 0.006675 | 0.009019 | 0.012139 | 0.011985 | 0.013895 | 0.027657 |
| min | 1.121335 | 1.087633 | 1.091697 | 1.101150 | 1.118839 | 1.097817 | 1.126527 | 1.113813 | 1.177764 | 1.168385 | 1.180782 | 1.175886 | 1.163198 | 1.170989 | 1.168906 | 1.121885 | 1.085560 | 1.106063 | 1.105548 | 1.105512 | 1.115861 | 1.122775 | 1.099711 | 1.104690 | 1.101493 | 1.122662 | 1.110892 | 1.095983 | 1.112748 | 1.098842 | 1.076367 | 1.129650 | 1.096421 | 1.114237 | 1.121225 | 1.120487 | 1.121091 | 1.130319 | 1.109137 | 1.130170 | 1.101953 | 1.116814 | 1.128458 | 1.129527 | 1.123929 | 1.131858 | 1.122993 | 1.109144 | 1.119223 | 1.091099 |
| 2.5% | 1.143506 | 1.124217 | 1.141994 | 1.130434 | 1.146722 | 1.128067 | 1.143370 | 1.144030 | 1.182810 | 1.171761 | 1.184687 | 1.183169 | 1.168423 | 1.174768 | 1.173613 | 1.126297 | 1.090317 | 1.110608 | 1.110233 | 1.110364 | 1.117911 | 1.125390 | 1.104458 | 1.128222 | 1.106128 | 1.127332 | 1.119259 | 1.113988 | 1.117357 | 1.131585 | 1.117621 | 1.133780 | 1.101387 | 1.118800 | 1.129669 | 1.128048 | 1.126541 | 1.135767 | 1.114105 | 1.135364 | 1.107491 | 1.124651 | 1.132583 | 1.134321 | 1.131716 | 1.135102 | 1.127282 | 1.128196 | 1.144799 | 1.105300 |
| 25% | 1.189496 | 1.171526 | 1.189998 | 1.169169 | 1.188757 | 1.169328 | 1.187897 | 1.184059 | 1.189325 | 1.175997 | 1.189014 | 1.191575 | 1.174682 | 1.181693 | 1.179739 | 1.130547 | 1.105556 | 1.117207 | 1.123158 | 1.130355 | 1.129954 | 1.130833 | 1.115516 | 1.148290 | 1.126247 | 1.148263 | 1.133001 | 1.146117 | 1.140261 | 1.151110 | 1.155526 | 1.135823 | 1.108858 | 1.120273 | 1.134159 | 1.142751 | 1.137128 | 1.141340 | 1.128710 | 1.143615 | 1.122823 | 1.132028 | 1.139657 | 1.142976 | 1.138795 | 1.141381 | 1.138414 | 1.157126 | 1.179512 | 1.169976 |
| 50% | 1.213636 | 1.184462 | 1.204967 | 1.179953 | 1.198067 | 1.180203 | 1.203699 | 1.195275 | 1.196981 | 1.181716 | 1.193319 | 1.199874 | 1.184606 | 1.185931 | 1.188038 | 1.133700 | 1.107946 | 1.120651 | 1.127070 | 1.137230 | 1.137664 | 1.135127 | 1.119281 | 1.158801 | 1.133107 | 1.161251 | 1.139756 | 1.161745 | 1.155334 | 1.163820 | 1.169287 | 1.141078 | 1.114666 | 1.124788 | 1.137081 | 1.143429 | 1.138507 | 1.142993 | 1.130560 | 1.150299 | 1.129048 | 1.152504 | 1.144513 | 1.148970 | 1.143015 | 1.149273 | 1.150807 | 1.163519 | 1.186228 | 1.182490 |
| 75% | 1.223951 | 1.202201 | 1.215094 | 1.193146 | 1.207661 | 1.192561 | 1.209913 | 1.200167 | 1.205482 | 1.190184 | 1.198580 | 1.206984 | 1.191806 | 1.190603 | 1.196341 | 1.138461 | 1.111079 | 1.125147 | 1.130902 | 1.140678 | 1.140630 | 1.139222 | 1.124283 | 1.166592 | 1.143270 | 1.165895 | 1.146970 | 1.181627 | 1.176484 | 1.175114 | 1.175762 | 1.158095 | 1.128693 | 1.157609 | 1.159073 | 1.150100 | 1.145600 | 1.168214 | 1.139408 | 1.171907 | 1.133522 | 1.160954 | 1.152330 | 1.155033 | 1.146711 | 1.154591 | 1.157689 | 1.168028 | 1.191293 | 1.199460 |
| 97.5% | 1.237011 | 1.216968 | 1.227870 | 1.211240 | 1.222818 | 1.207223 | 1.222281 | 1.213245 | 1.220786 | 1.202480 | 1.207922 | 1.216214 | 1.204522 | 1.204361 | 1.208362 | 1.158043 | 1.119779 | 1.140370 | 1.143867 | 1.148652 | 1.144836 | 1.152181 | 1.133085 | 1.175680 | 1.154311 | 1.176664 | 1.176441 | 1.192276 | 1.185548 | 1.181925 | 1.191386 | 1.179822 | 1.153446 | 1.180752 | 1.178504 | 1.156633 | 1.156839 | 1.184089 | 1.162578 | 1.184132 | 1.143226 | 1.174247 | 1.164784 | 1.165046 | 1.159510 | 1.170354 | 1.169076 | 1.176691 | 1.205888 | 1.203981 |
| max | 1.245073 | 1.228367 | 1.238126 | 1.224838 | 1.233165 | 1.215767 | 1.236797 | 1.231872 | 1.230489 | 1.229003 | 1.219486 | 1.237561 | 1.212615 | 1.213154 | 1.225060 | 1.162213 | 1.125571 | 1.150921 | 1.147316 | 1.161359 | 1.158233 | 1.164883 | 1.137418 | 1.180655 | 1.164497 | 1.196338 | 1.191306 | 1.200816 | 1.189473 | 1.199435 | 1.194131 | 1.182804 | 1.158220 | 1.185203 | 1.182401 | 1.168225 | 1.165617 | 1.187438 | 1.169199 | 1.200101 | 1.156490 | 1.185883 | 1.180713 | 1.171282 | 1.167475 | 1.185426 | 1.183058 | 1.192918 | 1.222750 | 1.210316 |
CapacityFactor_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.204529 | 1.179162 | 1.199145 | 1.178703 | 1.191829 | 1.174915 | 1.188556 | 1.133019 | 1.195355 | 1.182805 | 1.194073 | 1.199351 | 1.184094 | 1.186263 | 1.187475 | 1.136169 | 1.107723 | 1.122338 | 1.127000 | 1.134916 | 1.134973 | 1.135636 | 1.118875 | 1.156011 | 1.132513 | 1.153566 | 1.138641 | 1.158923 | 1.154248 | 1.161916 | 1.163659 | 1.147900 | 1.120182 | 1.137395 | 1.144948 | 1.144969 | 1.140668 | 1.152067 | 1.133856 | 1.155763 | 1.127355 | 1.147518 | 1.146115 | 1.148971 | 1.142986 | 1.148797 | 1.148411 | 1.161040 | 1.182093 | 1.115139 |
| std | 0.024238 | 0.023983 | 0.022216 | 0.019625 | 0.018371 | 0.019282 | 0.021344 | 0.032371 | 0.010971 | 0.012465 | 0.006744 | 0.009927 | 0.010498 | 0.007767 | 0.011217 | 0.008340 | 0.006504 | 0.007883 | 0.007305 | 0.009581 | 0.007684 | 0.006713 | 0.009343 | 0.014672 | 0.013208 | 0.020831 | 0.017619 | 0.024949 | 0.020972 | 0.015142 | 0.018738 | 0.015662 | 0.015037 | 0.020636 | 0.015276 | 0.007061 | 0.007439 | 0.015352 | 0.012024 | 0.015783 | 0.009178 | 0.015783 | 0.009202 | 0.008238 | 0.007065 | 0.009332 | 0.012174 | 0.011976 | 0.013936 | 0.019962 |
| min | 1.120788 | 1.079850 | 1.085268 | 1.100658 | 1.114405 | 1.097817 | 1.111950 | 0.888389 | 1.028827 | 0.884082 | 1.149327 | 1.151961 | 1.161977 | 1.137293 | 1.081241 | 1.121885 | 1.085560 | 1.106063 | 1.105548 | 1.105512 | 1.115861 | 1.122775 | 1.017208 | 1.090396 | 1.101230 | 1.012355 | 1.031510 | 1.013057 | 1.092556 | 1.098334 | 1.076367 | 1.129650 | 1.096421 | 1.114237 | 1.115666 | 1.120487 | 1.121091 | 1.130319 | 1.109137 | 1.074097 | 1.077102 | 1.076390 | 1.090193 | 1.117569 | 1.067044 | 1.092217 | 1.114946 | 1.109144 | 1.119223 | 1.048456 |
| 2.5% | 1.143038 | 1.121569 | 1.141994 | 1.130434 | 1.143149 | 1.128067 | 1.138832 | 1.040829 | 1.179028 | 1.171048 | 1.184064 | 1.181719 | 1.168358 | 1.173434 | 1.171708 | 1.125724 | 1.090036 | 1.109693 | 1.110233 | 1.110364 | 1.117911 | 1.125348 | 1.102124 | 1.121518 | 1.103481 | 1.114263 | 1.112008 | 1.108453 | 1.113518 | 1.125337 | 1.117587 | 1.133780 | 1.101387 | 1.118800 | 1.129481 | 1.128048 | 1.126541 | 1.135255 | 1.114105 | 1.134336 | 1.107376 | 1.124016 | 1.132517 | 1.134310 | 1.130949 | 1.134766 | 1.127271 | 1.128196 | 1.144330 | 1.060729 |
| 25% | 1.188635 | 1.167883 | 1.189242 | 1.168829 | 1.185477 | 1.166079 | 1.177383 | 1.125284 | 1.188061 | 1.175513 | 1.188836 | 1.191522 | 1.174580 | 1.180944 | 1.178372 | 1.130547 | 1.105556 | 1.117207 | 1.123158 | 1.130355 | 1.129743 | 1.130761 | 1.115211 | 1.146790 | 1.126147 | 1.144928 | 1.131693 | 1.145858 | 1.139857 | 1.150470 | 1.153129 | 1.135823 | 1.108858 | 1.120273 | 1.134159 | 1.142751 | 1.137128 | 1.141340 | 1.128676 | 1.143163 | 1.122625 | 1.131819 | 1.139582 | 1.142761 | 1.138592 | 1.141188 | 1.138109 | 1.157089 | 1.176190 | 1.115759 |
| 50% | 1.211987 | 1.180264 | 1.204051 | 1.179298 | 1.194885 | 1.178015 | 1.193439 | 1.144368 | 1.194612 | 1.180981 | 1.193181 | 1.199732 | 1.184385 | 1.185682 | 1.186584 | 1.133700 | 1.107946 | 1.120549 | 1.127025 | 1.137230 | 1.137359 | 1.135127 | 1.119269 | 1.158801 | 1.132935 | 1.161177 | 1.138335 | 1.159924 | 1.152007 | 1.162919 | 1.169110 | 1.141078 | 1.114666 | 1.124788 | 1.136634 | 1.143429 | 1.138507 | 1.142993 | 1.130560 | 1.149981 | 1.128915 | 1.151963 | 1.144482 | 1.148854 | 1.142780 | 1.148988 | 1.150511 | 1.163519 | 1.182417 | 1.121469 |
| 75% | 1.222805 | 1.198484 | 1.214829 | 1.192373 | 1.204099 | 1.189843 | 1.204682 | 1.153055 | 1.202788 | 1.189247 | 1.198355 | 1.206737 | 1.191586 | 1.190224 | 1.196128 | 1.138408 | 1.111079 | 1.124892 | 1.130902 | 1.140543 | 1.140559 | 1.139222 | 1.124107 | 1.166315 | 1.143270 | 1.165895 | 1.145713 | 1.181191 | 1.175697 | 1.174457 | 1.174458 | 1.158095 | 1.128693 | 1.157609 | 1.159073 | 1.150100 | 1.145369 | 1.168214 | 1.139408 | 1.171789 | 1.133405 | 1.160856 | 1.152330 | 1.155026 | 1.146532 | 1.154404 | 1.157427 | 1.168018 | 1.190338 | 1.124638 |
| 97.5% | 1.235927 | 1.214099 | 1.227146 | 1.210526 | 1.218021 | 1.200216 | 1.215746 | 1.164177 | 1.213279 | 1.201280 | 1.207819 | 1.216173 | 1.204424 | 1.204218 | 1.208354 | 1.158043 | 1.119779 | 1.140370 | 1.143867 | 1.148652 | 1.144810 | 1.152076 | 1.133085 | 1.175041 | 1.154311 | 1.176201 | 1.176441 | 1.192276 | 1.185548 | 1.181713 | 1.191386 | 1.179822 | 1.153446 | 1.180752 | 1.178504 | 1.156633 | 1.156121 | 1.183213 | 1.162578 | 1.184132 | 1.143226 | 1.174146 | 1.164784 | 1.165046 | 1.159510 | 1.170101 | 1.169076 | 1.176691 | 1.205603 | 1.143960 |
| max | 1.243720 | 1.224192 | 1.238126 | 1.224838 | 1.230139 | 1.213018 | 1.226526 | 1.180118 | 1.230489 | 1.229003 | 1.219486 | 1.237561 | 1.212615 | 1.213154 | 1.225060 | 1.162213 | 1.125571 | 1.150921 | 1.147316 | 1.161359 | 1.158233 | 1.164883 | 1.137418 | 1.180655 | 1.164497 | 1.195794 | 1.186844 | 1.200327 | 1.189473 | 1.199435 | 1.194131 | 1.182804 | 1.158220 | 1.185203 | 1.182401 | 1.168225 | 1.165617 | 1.187438 | 1.169199 | 1.200101 | 1.156490 | 1.185883 | 1.180713 | 1.171282 | 1.167475 | 1.185426 | 1.183058 | 1.192918 | 1.222284 | 1.158565 |
CapacityFactor_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.189025 | 1.102366 | 1.188702 | 1.163206 | 1.155698 | 1.088590 | 1.058886 | 1.006021 | 1.181391 | 1.174879 | 1.191812 | 1.197810 | 1.181147 | 1.180402 | 1.177827 | NaN | 1.106136 | 1.120949 | 1.125358 | 1.120891 | 1.131402 | 1.125259 | 1.110617 | 1.147151 | 1.122249 | 1.145595 | 1.128662 | 1.152964 | 1.148264 | 1.155995 | 1.159107 | 1.144980 | 1.118223 | 1.136256 | 1.141653 | 1.141546 | 1.134748 | 1.139543 | 1.124261 | 1.152743 | 1.124727 | 1.145859 | 1.145371 | 1.146326 | 1.139110 | 1.146444 | 1.146156 | 1.111360 | 1.066702 | 1.003447 |
| std | 0.026140 | 0.020379 | 0.022798 | 0.018983 | 0.017148 | 0.045331 | 0.037954 | 0.033595 | 0.038733 | 0.020365 | 0.007400 | 0.010150 | 0.011277 | 0.011917 | 0.022690 | NaN | 0.006360 | 0.007996 | 0.008030 | 0.008113 | 0.007740 | 0.007204 | 0.015917 | 0.019018 | 0.023053 | 0.026836 | 0.029680 | 0.031360 | 0.024818 | 0.019750 | 0.021955 | 0.014911 | 0.014708 | 0.020165 | 0.018399 | 0.010416 | 0.012275 | 0.014132 | 0.019897 | 0.016565 | 0.009498 | 0.016600 | 0.009783 | 0.009457 | 0.010316 | 0.011193 | 0.013337 | 0.013861 | 0.025168 | 0.025207 |
| min | 0.871278 | 1.023426 | 1.069489 | 1.082929 | 1.075824 | 0.980093 | 0.934079 | 0.856991 | 1.012902 | 0.946214 | 1.112109 | 1.149868 | 1.156994 | 1.122016 | 0.871683 | NaN | 1.083449 | 1.104467 | 1.095355 | 1.095401 | 1.111452 | 1.098569 | 1.001684 | 1.091744 | 1.023111 | 0.981707 | 0.941612 | 0.924574 | 1.036037 | 1.072700 | 1.037790 | 1.125375 | 1.087809 | 1.114237 | 1.064776 | 1.082820 | 1.069806 | 1.076009 | 1.033365 | 1.075188 | 1.090428 | 1.070664 | 1.088281 | 1.069534 | 1.050763 | 1.077570 | 1.095061 | 1.058853 | 0.994041 | 0.878038 |
| 2.5% | 1.132671 | 1.052230 | 1.133768 | 1.117694 | 1.107624 | 1.005251 | 0.987883 | 0.905791 | 1.056149 | 1.118297 | 1.179045 | 1.180961 | 1.162550 | 1.143031 | 1.126560 | NaN | 1.089282 | 1.107922 | 1.105428 | 1.099485 | 1.114434 | 1.112906 | 1.065619 | 1.112144 | 1.061928 | 1.076769 | 1.046179 | 1.075748 | 1.090821 | 1.104353 | 1.107945 | 1.129608 | 1.098420 | 1.118202 | 1.091877 | 1.105541 | 1.091808 | 1.094868 | 1.050550 | 1.120034 | 1.105676 | 1.118874 | 1.129771 | 1.130032 | 1.120846 | 1.128572 | 1.122146 | 1.071083 | 1.027922 | 0.966158 |
| 25% | 1.175651 | 1.091149 | 1.176971 | 1.152902 | 1.149150 | 1.044745 | 1.025620 | 0.995113 | 1.184190 | 1.172192 | 1.187320 | 1.189981 | 1.170801 | 1.177701 | 1.172594 | NaN | 1.103920 | 1.115506 | 1.121701 | 1.118043 | 1.126284 | 1.120221 | 1.107498 | 1.131756 | 1.112072 | 1.136082 | 1.123247 | 1.141951 | 1.136050 | 1.146016 | 1.148505 | 1.133735 | 1.108353 | 1.119975 | 1.133761 | 1.139954 | 1.131849 | 1.136840 | 1.122439 | 1.141475 | 1.118477 | 1.129928 | 1.138802 | 1.139733 | 1.134074 | 1.138279 | 1.135218 | 1.106169 | 1.040254 | 0.989202 |
| 50% | 1.195654 | 1.104376 | 1.193566 | 1.164574 | 1.159494 | 1.105928 | 1.074811 | 1.014365 | 1.192087 | 1.177798 | 1.191959 | 1.198316 | 1.181195 | 1.183031 | 1.179426 | NaN | 1.106373 | 1.119231 | 1.125867 | 1.122012 | 1.133806 | 1.125233 | 1.113376 | 1.150550 | 1.126589 | 1.154546 | 1.133703 | 1.157926 | 1.150349 | 1.157975 | 1.165342 | 1.138907 | 1.113642 | 1.123634 | 1.135139 | 1.141897 | 1.136527 | 1.140578 | 1.128395 | 1.146701 | 1.125715 | 1.150442 | 1.144014 | 1.146129 | 1.139845 | 1.147121 | 1.148016 | 1.113950 | 1.075006 | 0.993127 |
| 75% | 1.206106 | 1.117134 | 1.205331 | 1.177460 | 1.166578 | 1.127535 | 1.092994 | 1.029195 | 1.200978 | 1.184322 | 1.196060 | 1.205163 | 1.190367 | 1.186929 | 1.193776 | NaN | 1.109600 | 1.123784 | 1.129315 | 1.125343 | 1.136998 | 1.129334 | 1.118929 | 1.161798 | 1.138650 | 1.162892 | 1.142679 | 1.176584 | 1.169860 | 1.172109 | 1.171795 | 1.151636 | 1.125375 | 1.156411 | 1.154998 | 1.147233 | 1.140934 | 1.143326 | 1.131420 | 1.169666 | 1.131619 | 1.159576 | 1.151910 | 1.153367 | 1.144604 | 1.153107 | 1.155893 | 1.120561 | 1.086705 | 1.023757 |
| 97.5% | 1.218310 | 1.134244 | 1.217099 | 1.192547 | 1.179406 | 1.147901 | 1.104884 | 1.044061 | 1.212859 | 1.198513 | 1.206055 | 1.215055 | 1.201354 | 1.195658 | 1.204638 | NaN | 1.117626 | 1.139319 | 1.142357 | 1.132611 | 1.141159 | 1.143305 | 1.127765 | 1.174005 | 1.150487 | 1.174727 | 1.175116 | 1.190190 | 1.183106 | 1.181234 | 1.189887 | 1.176233 | 1.149954 | 1.180181 | 1.176078 | 1.154360 | 1.150019 | 1.169545 | 1.151111 | 1.182641 | 1.141430 | 1.173405 | 1.164520 | 1.163896 | 1.158534 | 1.169578 | 1.168605 | 1.129871 | 1.108533 | 1.050959 |
| max | 1.226578 | 1.145995 | 1.230224 | 1.204673 | 1.192237 | 1.154679 | 1.123683 | 1.061693 | 1.230082 | 1.228479 | 1.218594 | 1.235745 | 1.209214 | 1.210166 | 1.217070 | NaN | 1.123325 | 1.149848 | 1.145687 | 1.147052 | 1.154194 | 1.151005 | 1.131445 | 1.180655 | 1.162620 | 1.189540 | 1.186265 | 1.199350 | 1.188564 | 1.198408 | 1.194131 | 1.179590 | 1.155850 | 1.184084 | 1.180544 | 1.165909 | 1.156939 | 1.177256 | 1.161518 | 1.197883 | 1.155884 | 1.184563 | 1.180499 | 1.170763 | 1.166487 | 1.185426 | 1.182520 | 1.146181 | 1.124463 | 1.076046 |
Revenue_track(def)/fixed(optcf)_hist,da,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.181917 | 1.178839 | 1.185226 | 1.183955 | 1.214425 | 1.215919 | 1.244379 | 1.283981 | 1.187774 | 1.182895 | 1.202573 | 1.202635 | 1.187485 | 1.174135 | 1.181507 | 1.127146 | 1.101453 | 1.103054 | 1.060832 | 1.041260 | 1.076714 | 1.109886 | 1.076873 | 1.157579 | 1.147464 | 1.163556 | 1.145376 | 1.154716 | 1.159442 | 1.158680 | 1.165527 | 1.144735 | 1.126639 | 1.128722 | 1.119979 | 1.075455 | 1.106741 | 1.141804 | 1.119153 | 1.159325 | 1.142768 | 1.147064 | 1.153731 | 1.111158 | 1.135407 | 1.145694 | 1.142384 | 1.204890 | 1.244522 | 1.286184 |
| std | 0.022381 | 0.023081 | 0.026295 | 0.020538 | 0.022739 | 0.030419 | 0.031715 | 0.037652 | 0.008695 | 0.012378 | 0.005544 | 0.009585 | 0.007014 | 0.005457 | 0.007369 | 0.009825 | 0.008149 | 0.008444 | 0.014137 | 0.010555 | 0.011574 | 0.007588 | 0.013086 | 0.016710 | 0.013108 | 0.013659 | 0.014171 | 0.035206 | 0.020213 | 0.013709 | 0.019900 | 0.018879 | 0.015191 | 0.020859 | 0.026342 | 0.022043 | 0.016630 | 0.023355 | 0.017076 | 0.014434 | 0.007885 | 0.012586 | 0.008340 | 0.020596 | 0.011897 | 0.007030 | 0.011863 | 0.014780 | 0.019459 | 0.037499 |
| min | 1.110268 | 1.085192 | 1.069278 | 1.104117 | 1.132645 | 1.140847 | 1.139030 | 1.161767 | 1.140965 | 1.117358 | 1.178982 | 1.152874 | 1.165554 | 1.144097 | 1.138525 | 1.108089 | 1.073062 | 1.078627 | 1.022068 | 1.009493 | 1.045780 | 1.095654 | 1.028590 | 1.093155 | 1.112657 | 1.127029 | 1.104380 | 1.068294 | 1.109238 | 1.094050 | 1.068217 | 1.105689 | 1.099962 | 1.099054 | 1.074313 | 1.028457 | 1.069932 | 1.110392 | 1.074899 | 1.125543 | 1.102139 | 1.117784 | 1.122668 | 1.049814 | 1.091526 | 1.115595 | 1.107168 | 1.137132 | 1.146840 | 1.169234 |
| 2.5% | 1.126661 | 1.120422 | 1.126192 | 1.135459 | 1.161462 | 1.157936 | 1.161781 | 1.194076 | 1.172540 | 1.169058 | 1.194072 | 1.183727 | 1.175063 | 1.163306 | 1.168623 | 1.113803 | 1.078222 | 1.086865 | 1.029663 | 1.018334 | 1.051404 | 1.098115 | 1.047585 | 1.120548 | 1.120203 | 1.135910 | 1.118632 | 1.091836 | 1.126835 | 1.125884 | 1.117741 | 1.121377 | 1.104204 | 1.104092 | 1.081831 | 1.042282 | 1.071731 | 1.112174 | 1.082637 | 1.136393 | 1.124865 | 1.126038 | 1.135481 | 1.066365 | 1.111233 | 1.132726 | 1.119322 | 1.170746 | 1.198473 | 1.188146 |
| 25% | 1.167852 | 1.167889 | 1.168783 | 1.173048 | 1.202742 | 1.195185 | 1.224837 | 1.265940 | 1.182747 | 1.178228 | 1.198743 | 1.195959 | 1.182244 | 1.171368 | 1.177249 | 1.120303 | 1.099305 | 1.097383 | 1.052952 | 1.036918 | 1.069549 | 1.103741 | 1.070207 | 1.150212 | 1.139108 | 1.153661 | 1.139538 | 1.122667 | 1.142036 | 1.149617 | 1.153923 | 1.129585 | 1.117118 | 1.109287 | 1.101258 | 1.062077 | 1.099321 | 1.123670 | 1.113603 | 1.147931 | 1.138369 | 1.134901 | 1.148977 | 1.099807 | 1.128734 | 1.140771 | 1.132811 | 1.197144 | 1.236193 | 1.277404 |
| 50% | 1.188600 | 1.179614 | 1.188976 | 1.184977 | 1.213929 | 1.213394 | 1.248827 | 1.291287 | 1.186917 | 1.181531 | 1.201803 | 1.203752 | 1.187814 | 1.174446 | 1.181682 | 1.124925 | 1.101876 | 1.102166 | 1.058814 | 1.041229 | 1.081304 | 1.109817 | 1.076754 | 1.162706 | 1.148362 | 1.166099 | 1.145342 | 1.152205 | 1.156076 | 1.160812 | 1.171774 | 1.140239 | 1.121465 | 1.124168 | 1.108983 | 1.065022 | 1.104415 | 1.129348 | 1.120036 | 1.155738 | 1.144011 | 1.150721 | 1.154610 | 1.116371 | 1.136464 | 1.145575 | 1.145315 | 1.206318 | 1.247765 | 1.299170 |
| 75% | 1.199382 | 1.196324 | 1.205633 | 1.200130 | 1.232974 | 1.241071 | 1.266993 | 1.308802 | 1.191289 | 1.185908 | 1.205589 | 1.209090 | 1.192114 | 1.177406 | 1.185309 | 1.131823 | 1.105781 | 1.108163 | 1.069954 | 1.047729 | 1.084889 | 1.114889 | 1.086376 | 1.168968 | 1.157750 | 1.173450 | 1.151059 | 1.185843 | 1.179784 | 1.169377 | 1.179369 | 1.159060 | 1.135580 | 1.146621 | 1.146868 | 1.090156 | 1.119276 | 1.165085 | 1.127829 | 1.172926 | 1.148169 | 1.157647 | 1.159238 | 1.126602 | 1.141845 | 1.149953 | 1.151302 | 1.215337 | 1.258016 | 1.312071 |
| 97.5% | 1.211921 | 1.213284 | 1.224514 | 1.215376 | 1.251071 | 1.265296 | 1.296627 | 1.341588 | 1.210202 | 1.204802 | 1.214694 | 1.219197 | 1.201028 | 1.183961 | 1.197186 | 1.154044 | 1.115509 | 1.121290 | 1.088744 | 1.059851 | 1.090714 | 1.125500 | 1.101213 | 1.178468 | 1.168269 | 1.185894 | 1.180674 | 1.207165 | 1.192289 | 1.176136 | 1.189863 | 1.182637 | 1.158439 | 1.168053 | 1.168654 | 1.123232 | 1.136183 | 1.184847 | 1.147870 | 1.184392 | 1.154991 | 1.166954 | 1.168214 | 1.140186 | 1.161733 | 1.160864 | 1.162110 | 1.227137 | 1.273545 | 1.330375 |
| max | 1.218533 | 1.224095 | 1.243719 | 1.252418 | 1.261841 | 1.338387 | 1.355752 | 1.426787 | 1.230381 | 1.384686 | 1.223727 | 1.233995 | 1.210145 | 1.191988 | 1.218830 | 1.155768 | 1.123286 | 1.129567 | 1.092672 | 1.078632 | 1.104741 | 1.141944 | 1.109250 | 1.186720 | 1.181845 | 1.193939 | 1.188060 | 1.216253 | 1.198028 | 1.187579 | 1.196287 | 1.184335 | 1.165063 | 1.173515 | 1.171687 | 1.140102 | 1.153492 | 1.191377 | 1.155215 | 1.198492 | 1.167081 | 1.211865 | 1.180472 | 1.158249 | 1.171540 | 1.175216 | 1.176106 | 1.250215 | 1.300944 | 1.346755 |
Revenue_track(def)/fixed(optcf)_hist,rt,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.169654 | 1.154341 | 1.208358 | 1.184296 | 1.233457 | 1.231424 | 1.270990 | 1.314155 | 1.173679 | 1.173916 | 1.196387 | 1.197151 | 1.185748 | 1.171324 | 1.182024 | NaN | 1.104696 | 1.112299 | 1.072420 | 1.046171 | 1.095428 | 1.131191 | 1.089367 | 1.160614 | 1.148633 | 1.158114 | 1.148857 | 1.161811 | 1.161352 | 1.162536 | 1.161656 | 1.147699 | 1.127997 | 1.136101 | 1.129810 | 1.068539 | 1.116495 | 1.155259 | 1.129516 | 1.164757 | 1.149264 | 1.152389 | 1.155133 | 1.106295 | 1.136208 | 1.150129 | 1.140551 | 1.202060 | 1.271793 | 1.303549 |
| std | 0.024551 | 0.021519 | 0.054546 | 0.023076 | 0.026786 | 0.035793 | 0.042620 | 0.052336 | 0.008318 | 0.016602 | 0.006015 | 0.012716 | 0.011086 | 0.007551 | 0.010428 | NaN | 0.009310 | 0.008191 | 0.015197 | 0.010893 | 0.013029 | 0.008582 | 0.018026 | 0.015180 | 0.012179 | 0.014574 | 0.016503 | 0.031269 | 0.023926 | 0.013250 | 0.019064 | 0.015977 | 0.012438 | 0.013231 | 0.021404 | 0.023542 | 0.017389 | 0.020392 | 0.022699 | 0.013102 | 0.006236 | 0.012919 | 0.010051 | 0.020377 | 0.011963 | 0.007382 | 0.014120 | 0.014677 | 0.041665 | 0.102732 |
| min | 1.006985 | 1.071476 | 1.042955 | 1.091650 | 1.147781 | 1.140549 | 1.153979 | 1.171260 | 1.142763 | 1.096398 | 1.173103 | 1.120406 | 1.144171 | 1.117077 | 1.126516 | NaN | 1.074328 | 1.085771 | 1.023092 | 1.017849 | 1.063995 | 1.112637 | 1.018506 | 1.105374 | 1.113477 | 1.125920 | 1.105038 | 1.075332 | 1.118396 | 1.096191 | 1.063944 | 1.112800 | 1.090459 | 1.110625 | 1.076052 | 1.020760 | 1.076817 | 1.122341 | 1.068640 | 1.100543 | 1.113560 | 1.110421 | 1.113949 | 1.043976 | 1.073069 | 1.093460 | 1.084628 | 1.140601 | 1.145627 | 1.069073 |
| 2.5% | 1.109924 | 1.105358 | 1.114778 | 1.131957 | 1.173938 | 1.157547 | 1.178197 | 1.204142 | 1.159496 | 1.142274 | 1.184788 | 1.168424 | 1.165726 | 1.155328 | 1.159218 | NaN | 1.079061 | 1.093316 | 1.034982 | 1.024174 | 1.069512 | 1.116459 | 1.046464 | 1.128266 | 1.120357 | 1.129197 | 1.119101 | 1.105837 | 1.127902 | 1.131637 | 1.117100 | 1.129621 | 1.106916 | 1.117195 | 1.085630 | 1.033193 | 1.078209 | 1.127177 | 1.080325 | 1.144490 | 1.136695 | 1.130513 | 1.131459 | 1.066072 | 1.114611 | 1.136962 | 1.112858 | 1.164553 | 1.189393 | 1.088072 |
| 25% | 1.153125 | 1.142573 | 1.174226 | 1.172601 | 1.217273 | 1.208121 | 1.241875 | 1.281787 | 1.168915 | 1.167611 | 1.192499 | 1.190561 | 1.178280 | 1.168170 | 1.177065 | NaN | 1.101979 | 1.108413 | 1.066753 | 1.039922 | 1.084421 | 1.125024 | 1.080933 | 1.150384 | 1.141119 | 1.149191 | 1.139479 | 1.137470 | 1.142381 | 1.154622 | 1.150284 | 1.133923 | 1.121321 | 1.125005 | 1.114247 | 1.050970 | 1.106604 | 1.140869 | 1.117715 | 1.155709 | 1.145518 | 1.140709 | 1.149404 | 1.089789 | 1.129420 | 1.145704 | 1.130677 | 1.194390 | 1.245417 | 1.276665 |
| 50% | 1.177229 | 1.155977 | 1.205031 | 1.184263 | 1.233573 | 1.227655 | 1.274670 | 1.321106 | 1.173355 | 1.171462 | 1.196311 | 1.199399 | 1.185337 | 1.172251 | 1.182709 | NaN | 1.105066 | 1.111289 | 1.072903 | 1.044929 | 1.100862 | 1.130612 | 1.092580 | 1.165641 | 1.150188 | 1.159714 | 1.150395 | 1.159114 | 1.156230 | 1.163779 | 1.169203 | 1.147034 | 1.123311 | 1.130362 | 1.126810 | 1.055274 | 1.113386 | 1.153420 | 1.135918 | 1.161721 | 1.149328 | 1.155261 | 1.156055 | 1.112866 | 1.136088 | 1.149887 | 1.143157 | 1.203407 | 1.276794 | 1.348387 |
| 75% | 1.187771 | 1.170526 | 1.229244 | 1.199650 | 1.252196 | 1.257081 | 1.296987 | 1.346624 | 1.177926 | 1.176793 | 1.200201 | 1.205410 | 1.191938 | 1.175402 | 1.186882 | NaN | 1.110385 | 1.116882 | 1.081059 | 1.053966 | 1.105559 | 1.136526 | 1.101839 | 1.170971 | 1.156911 | 1.167118 | 1.155590 | 1.183938 | 1.180108 | 1.172579 | 1.174424 | 1.157568 | 1.136992 | 1.145586 | 1.145472 | 1.088729 | 1.131117 | 1.169705 | 1.146466 | 1.175863 | 1.153347 | 1.163316 | 1.161215 | 1.122066 | 1.142615 | 1.154550 | 1.151247 | 1.212967 | 1.306846 | 1.369944 |
| 97.5% | 1.200277 | 1.188287 | 1.359989 | 1.225336 | 1.283092 | 1.299632 | 1.348842 | 1.401838 | 1.188946 | 1.216728 | 1.208436 | 1.214098 | 1.208614 | 1.184376 | 1.201019 | NaN | 1.118901 | 1.128558 | 1.097478 | 1.066718 | 1.111466 | 1.149157 | 1.118047 | 1.179885 | 1.169412 | 1.186294 | 1.182577 | 1.215038 | 1.218906 | 1.183850 | 1.186180 | 1.180483 | 1.153047 | 1.164782 | 1.167830 | 1.113029 | 1.145731 | 1.198272 | 1.161728 | 1.188348 | 1.160981 | 1.172055 | 1.173438 | 1.134513 | 1.161905 | 1.164451 | 1.163397 | 1.224471 | 1.330041 | 1.413639 |
| max | 1.209193 | 1.197676 | 1.413043 | 1.243834 | 1.300952 | 1.361951 | 1.438653 | 1.541156 | 1.271035 | 1.275370 | 1.230533 | 1.246152 | 1.241951 | 1.198710 | 1.242655 | NaN | 1.127995 | 1.136601 | 1.100520 | 1.087814 | 1.129040 | 1.168823 | 1.126912 | 1.191849 | 1.176035 | 1.191777 | 1.230359 | 1.222688 | 1.236867 | 1.195777 | 1.199620 | 1.183515 | 1.159652 | 1.172011 | 1.169610 | 1.134068 | 1.159474 | 1.205303 | 1.163344 | 1.200596 | 1.169452 | 1.247257 | 1.188343 | 1.160664 | 1.181872 | 1.195529 | 1.185199 | 1.250999 | 1.358766 | 1.728387 |
Revenue_track(def)/fixed(optcf)_hist,da,curtail,baselinemustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.182043 | 1.179648 | 1.186015 | 1.184061 | 1.214618 | 1.216763 | 1.246307 | 1.297633 | 1.188637 | 1.183512 | 1.202692 | 1.203048 | 1.187615 | 1.174828 | 1.182178 | 1.127156 | 1.101453 | 1.103063 | 1.060845 | 1.041266 | 1.076749 | 1.109920 | 1.077146 | 1.157944 | 1.147570 | 1.165519 | 1.147275 | 1.155698 | 1.160632 | 1.159049 | 1.166140 | 1.144736 | 1.126639 | 1.128729 | 1.120038 | 1.075455 | 1.106756 | 1.141818 | 1.119160 | 1.159573 | 1.142848 | 1.147216 | 1.153808 | 1.111247 | 1.135602 | 1.145810 | 1.142466 | 1.204891 | 1.244844 | 1.296076 |
| std | 0.022436 | 0.022878 | 0.026940 | 0.020555 | 0.022758 | 0.030341 | 0.033055 | 0.051749 | 0.011987 | 0.021364 | 0.005614 | 0.010297 | 0.007131 | 0.006827 | 0.008244 | 0.009816 | 0.008149 | 0.008451 | 0.014137 | 0.010559 | 0.011565 | 0.007752 | 0.014352 | 0.016670 | 0.013128 | 0.016587 | 0.019464 | 0.035485 | 0.021923 | 0.013660 | 0.020355 | 0.018878 | 0.015191 | 0.020855 | 0.026377 | 0.022043 | 0.016655 | 0.023383 | 0.017083 | 0.014958 | 0.008039 | 0.012701 | 0.008420 | 0.020533 | 0.012298 | 0.007105 | 0.011895 | 0.014781 | 0.019637 | 0.040425 |
| min | 1.110286 | 1.089072 | 1.072294 | 1.104159 | 1.132682 | 1.140847 | 1.139030 | 1.166622 | 1.166298 | 1.121188 | 1.179144 | 1.152874 | 1.165554 | 1.144097 | 1.138525 | 1.108089 | 1.073062 | 1.078627 | 1.022068 | 1.009493 | 1.045806 | 1.095654 | 1.034497 | 1.093155 | 1.112779 | 1.134035 | 1.104380 | 1.068294 | 1.109246 | 1.094050 | 1.068217 | 1.105689 | 1.099962 | 1.099054 | 1.074313 | 1.028457 | 1.069932 | 1.110392 | 1.074899 | 1.125543 | 1.102139 | 1.117784 | 1.122668 | 1.049814 | 1.091526 | 1.118942 | 1.107168 | 1.137132 | 1.146840 | 1.174869 |
| 2.5% | 1.126724 | 1.120590 | 1.126192 | 1.135459 | 1.161889 | 1.158449 | 1.161781 | 1.201355 | 1.174043 | 1.169548 | 1.194238 | 1.183727 | 1.175075 | 1.164058 | 1.169017 | 1.113803 | 1.078222 | 1.086865 | 1.029663 | 1.018334 | 1.051425 | 1.098115 | 1.048867 | 1.120548 | 1.120204 | 1.136947 | 1.118632 | 1.092225 | 1.126852 | 1.126827 | 1.117911 | 1.121377 | 1.104204 | 1.104092 | 1.081831 | 1.042282 | 1.071731 | 1.112174 | 1.082637 | 1.136403 | 1.124865 | 1.126156 | 1.135501 | 1.066365 | 1.111233 | 1.132758 | 1.119421 | 1.170746 | 1.198474 | 1.194159 |
| 25% | 1.167908 | 1.169092 | 1.168796 | 1.173148 | 1.202881 | 1.196596 | 1.225445 | 1.275987 | 1.183082 | 1.178367 | 1.198796 | 1.196061 | 1.182304 | 1.171603 | 1.177390 | 1.120303 | 1.099305 | 1.097383 | 1.052953 | 1.036918 | 1.069903 | 1.103741 | 1.070228 | 1.150233 | 1.139163 | 1.155952 | 1.139616 | 1.122667 | 1.142576 | 1.150155 | 1.154145 | 1.129585 | 1.117118 | 1.109287 | 1.101258 | 1.062077 | 1.099321 | 1.123670 | 1.113603 | 1.147970 | 1.138383 | 1.134991 | 1.148977 | 1.100144 | 1.128937 | 1.140867 | 1.132978 | 1.197144 | 1.236200 | 1.284325 |
| 50% | 1.188673 | 1.180388 | 1.189432 | 1.184996 | 1.214147 | 1.214550 | 1.250143 | 1.301618 | 1.187117 | 1.181629 | 1.201955 | 1.203867 | 1.187864 | 1.174605 | 1.181987 | 1.124925 | 1.101876 | 1.102182 | 1.058814 | 1.041229 | 1.081320 | 1.109817 | 1.076754 | 1.162711 | 1.148452 | 1.166617 | 1.145752 | 1.152388 | 1.158087 | 1.161288 | 1.171930 | 1.140239 | 1.121465 | 1.124168 | 1.108983 | 1.065022 | 1.104415 | 1.129348 | 1.120036 | 1.155748 | 1.144040 | 1.150815 | 1.154622 | 1.116469 | 1.136532 | 1.145645 | 1.145348 | 1.206318 | 1.247888 | 1.308847 |
| 75% | 1.199501 | 1.196914 | 1.206448 | 1.200204 | 1.233217 | 1.241513 | 1.269006 | 1.318441 | 1.191564 | 1.186251 | 1.205655 | 1.209477 | 1.192135 | 1.177604 | 1.185677 | 1.131823 | 1.105781 | 1.108163 | 1.070366 | 1.047729 | 1.084905 | 1.114889 | 1.086376 | 1.169065 | 1.158317 | 1.173687 | 1.152219 | 1.186845 | 1.179868 | 1.169377 | 1.179844 | 1.159060 | 1.135580 | 1.146621 | 1.146868 | 1.090156 | 1.119276 | 1.165085 | 1.127829 | 1.173245 | 1.148255 | 1.157762 | 1.159284 | 1.126674 | 1.141921 | 1.150076 | 1.151367 | 1.215337 | 1.258657 | 1.324756 |
| 97.5% | 1.211997 | 1.214114 | 1.232054 | 1.215376 | 1.251221 | 1.266347 | 1.301310 | 1.377570 | 1.210589 | 1.205101 | 1.215214 | 1.220971 | 1.201152 | 1.186844 | 1.199451 | 1.154044 | 1.115509 | 1.121290 | 1.088744 | 1.059851 | 1.090727 | 1.125500 | 1.101213 | 1.178617 | 1.168693 | 1.189270 | 1.181707 | 1.207165 | 1.196373 | 1.176659 | 1.193189 | 1.182637 | 1.158439 | 1.168053 | 1.168654 | 1.123232 | 1.136316 | 1.184847 | 1.147870 | 1.185050 | 1.155127 | 1.167419 | 1.168509 | 1.140186 | 1.162369 | 1.161283 | 1.162441 | 1.227137 | 1.273545 | 1.343914 |
| max | 1.218605 | 1.225300 | 1.266654 | 1.252573 | 1.261857 | 1.338387 | 1.407147 | 1.889847 | 1.413759 | 1.904386 | 1.225089 | 1.272803 | 1.218542 | 1.249642 | 1.266207 | 1.155768 | 1.123286 | 1.129567 | 1.092672 | 1.078632 | 1.104761 | 1.162642 | 1.274044 | 1.186720 | 1.181845 | 1.281184 | 1.279305 | 1.219495 | 1.271597 | 1.189986 | 1.230165 | 1.184335 | 1.165063 | 1.173515 | 1.171687 | 1.140102 | 1.153492 | 1.193383 | 1.155215 | 1.269284 | 1.202461 | 1.212101 | 1.194328 | 1.158249 | 1.285252 | 1.179436 | 1.176106 | 1.250215 | 1.300946 | 1.379000 |
Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.178439 | 1.177863 | 1.232668 | 1.200964 | 1.264744 | 1.329904 | 1.364294 | 1.447377 | 1.179093 | 1.178177 | 1.198053 | 1.198947 | 1.188937 | 1.175484 | 1.190139 | NaN | 1.104925 | 1.112464 | 1.072453 | 1.046351 | 1.099406 | 1.146547 | 1.099272 | 1.168344 | 1.155910 | 1.172304 | 1.164316 | 1.171963 | 1.171236 | 1.170415 | 1.168777 | 1.153040 | 1.130702 | 1.138563 | 1.133497 | 1.071333 | 1.121176 | 1.180530 | 1.146732 | 1.167541 | 1.150573 | 1.153609 | 1.155887 | 1.108226 | 1.140905 | 1.152678 | 1.144233 | 1.275783 | 1.389333 | 1.553425 |
| std | 0.027015 | 0.030475 | 0.057318 | 0.028671 | 0.037703 | 0.108091 | 0.070678 | 0.131262 | 0.014328 | 0.017017 | 0.008258 | 0.013661 | 0.014106 | 0.011341 | 0.023930 | NaN | 0.009608 | 0.008220 | 0.015134 | 0.010889 | 0.012958 | 0.011298 | 0.024892 | 0.019234 | 0.015771 | 0.041091 | 0.061852 | 0.047876 | 0.043002 | 0.021388 | 0.029355 | 0.018729 | 0.013241 | 0.014282 | 0.024717 | 0.024995 | 0.021854 | 0.065404 | 0.045681 | 0.018812 | 0.008011 | 0.014709 | 0.010495 | 0.021537 | 0.016356 | 0.012042 | 0.012522 | 0.017688 | 0.047597 | 0.253418 |
| min | 1.100273 | 1.084651 | 1.121712 | 1.111368 | 1.163002 | 1.167293 | 1.218189 | 1.260330 | 1.151498 | 1.122557 | 1.178177 | 1.121664 | 1.144595 | 1.117327 | 1.126555 | NaN | 1.074328 | 1.085787 | 1.023394 | 1.018355 | 1.069441 | 1.127823 | 1.051757 | 1.107984 | 1.126440 | 1.126247 | 1.106232 | 1.079000 | 1.119753 | 1.101725 | 1.071013 | 1.114136 | 1.091101 | 1.110951 | 1.077105 | 1.021930 | 1.078370 | 1.127029 | 1.075562 | 1.101132 | 1.113601 | 1.110514 | 1.114429 | 1.044128 | 1.077368 | 1.102866 | 1.103553 | 1.208960 | 1.260082 | 1.178964 |
| 2.5% | 1.117229 | 1.115872 | 1.148914 | 1.148406 | 1.195190 | 1.203775 | 1.237562 | 1.298872 | 1.163267 | 1.157703 | 1.186528 | 1.174288 | 1.167959 | 1.157496 | 1.162582 | NaN | 1.079061 | 1.093329 | 1.035135 | 1.024595 | 1.073440 | 1.131499 | 1.063614 | 1.135605 | 1.130110 | 1.134388 | 1.120300 | 1.112993 | 1.130946 | 1.141796 | 1.125538 | 1.130803 | 1.109475 | 1.117580 | 1.088213 | 1.033971 | 1.079762 | 1.130648 | 1.087624 | 1.145915 | 1.136771 | 1.131594 | 1.132567 | 1.068281 | 1.120397 | 1.138247 | 1.122132 | 1.236337 | 1.313398 | 1.199461 |
| 25% | 1.159752 | 1.159195 | 1.194247 | 1.186304 | 1.239815 | 1.245347 | 1.321760 | 1.369893 | 1.171484 | 1.169235 | 1.193315 | 1.192022 | 1.180092 | 1.170818 | 1.179665 | NaN | 1.101979 | 1.108559 | 1.066753 | 1.040034 | 1.088374 | 1.139281 | 1.089075 | 1.159510 | 1.144895 | 1.157308 | 1.145069 | 1.141612 | 1.146545 | 1.158134 | 1.154253 | 1.136158 | 1.122927 | 1.126308 | 1.114778 | 1.052257 | 1.108357 | 1.143558 | 1.121861 | 1.156839 | 1.146429 | 1.141269 | 1.149915 | 1.090831 | 1.132708 | 1.147539 | 1.134384 | 1.267693 | 1.352741 | 1.381610 |
| 50% | 1.186409 | 1.178076 | 1.225690 | 1.197539 | 1.257045 | 1.279313 | 1.349841 | 1.422102 | 1.175964 | 1.173431 | 1.197066 | 1.200431 | 1.187207 | 1.174532 | 1.185653 | NaN | 1.105148 | 1.111668 | 1.072903 | 1.044991 | 1.104563 | 1.145583 | 1.098862 | 1.171407 | 1.156612 | 1.169032 | 1.154342 | 1.169862 | 1.161805 | 1.169550 | 1.172432 | 1.149936 | 1.126299 | 1.132100 | 1.130936 | 1.060015 | 1.115801 | 1.158917 | 1.141490 | 1.164177 | 1.150336 | 1.155768 | 1.156580 | 1.114013 | 1.139411 | 1.151424 | 1.145191 | 1.277179 | 1.376338 | 1.525901 |
| 75% | 1.197607 | 1.195866 | 1.250536 | 1.214697 | 1.292704 | 1.408467 | 1.391693 | 1.489774 | 1.182898 | 1.181590 | 1.201352 | 1.206795 | 1.194422 | 1.178572 | 1.196103 | NaN | 1.110620 | 1.117304 | 1.081059 | 1.054111 | 1.109681 | 1.150463 | 1.107311 | 1.176549 | 1.163593 | 1.178661 | 1.163972 | 1.195431 | 1.186416 | 1.176941 | 1.178334 | 1.166455 | 1.138550 | 1.149683 | 1.151019 | 1.091878 | 1.136092 | 1.194176 | 1.159051 | 1.178559 | 1.154446 | 1.163891 | 1.161792 | 1.122882 | 1.146431 | 1.156309 | 1.152424 | 1.284167 | 1.431716 | 1.806245 |
| 97.5% | 1.210492 | 1.236665 | 1.391501 | 1.268023 | 1.347535 | 1.582260 | 1.520961 | 1.680171 | 1.210388 | 1.227332 | 1.216783 | 1.219348 | 1.226082 | 1.198987 | 1.236174 | NaN | 1.121525 | 1.128558 | 1.097478 | 1.066789 | 1.115263 | 1.179902 | 1.131562 | 1.201694 | 1.196771 | 1.238451 | 1.249975 | 1.246872 | 1.237129 | 1.223418 | 1.208823 | 1.189883 | 1.157949 | 1.166976 | 1.193062 | 1.115763 | 1.167620 | 1.432421 | 1.323875 | 1.193098 | 1.164742 | 1.175531 | 1.175892 | 1.140910 | 1.168466 | 1.172054 | 1.169777 | 1.312614 | 1.470020 | 1.978326 |
| max | 1.423716 | 1.302028 | 1.544809 | 1.317285 | 1.418395 | 1.639772 | 1.834946 | 2.660819 | 1.392086 | 1.282667 | 1.299695 | 1.277193 | 1.269242 | 1.310014 | 1.584219 | NaN | 1.134053 | 1.136601 | 1.100520 | 1.087872 | 1.133987 | 1.229645 | 1.546495 | 1.258766 | 1.215248 | 1.580827 | 1.678640 | 1.627221 | 1.574349 | 1.324488 | 1.464246 | 1.193524 | 1.163234 | 1.181576 | 1.222366 | 1.175926 | 1.237520 | 1.476765 | 1.379264 | 1.716022 | 1.280244 | 1.290533 | 1.243708 | 1.267371 | 1.435168 | 1.342671 | 1.240983 | 1.373498 | 1.607390 | 2.703056 |
########## Starting absolute values for track CF-opt
### Data-indexed parameters
data = [
'OptCF_Azimuth(da)(mustrun)',
'OptRev_Azimuth(da)(mustrun)',
'OptRev_Azimuth(rt)(mustrun)',
'OptRev_Azimuth(da)(curtail)',
'OptRev_Azimuth(rt)(curtail)',
'OptCF_Tilt(da)(mustrun)',
'OptRev_Tilt(da)(mustrun)',
'OptRev_Tilt(rt)(mustrun)',
'OptRev_Tilt(da)(curtail)',
'OptRev_Tilt(rt)(curtail)',
]
colindex = [0, 1, 1, 2, 2, 3, 4, 4, 5, 5,]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right',
'right','left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],
mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35,
0.7, 0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [[2011,2017],slice(None),slice(None),slice(None),slice(None),
[2011,2017],slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))
### Column-indexed parameters
ylim = [
[166, 255],
[166, 255],
[166, 255],
[15, 57],
[15, 57],
[15, 57],
]
xlim = [
[2017, 2018.9],
[2009.4, 2018],
[2009.4, 2018],
[2017, 2018.9],
[2009.4, 2018],
[2009.4, 2018],
]
majlocs = [30, 30, 30, 15, 15, 15, ]
minlocs = [2, 2, 2, 3, 3, 3, ]
ylabel = [
'Azimuth',
'Azimuth',
'Azimuth',
'Tilt',
'Tilt',
'Tilt',
]
note = [
'CF Opt.',
'Revenue Opt. (must-run)',
'Revenue Opt. (curtailable)',
'CF Opt.',
'Revenue Opt. (must-run)',
'Revenue Opt. (curtailable)',
]
y1 = 1.2 # 1.2 if using note, 1 if no note
y2 = 1.07 # 1.07 if using note, 1.04 if no note
gridspec_kw = {'width_ratios': [0.2, 2, 2, 0.2, 2, 2,], 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=False, gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]],
density=True, bootstrap=bootstrap,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
format_axes=False,
)
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
ax[(row,col)].set_xlim(*xlim[col])
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(-1,1)].legend(
handles=patches, loc='upper left', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Optimized orientation, fixed-tilt', weight='bold', y=y2, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
print(datum)
display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
| OptCF_Azimuth(da)(mustrun) | OptRev_Azimuth(da)(mustrun) | OptRev_Azimuth(rt)(mustrun) | OptRev_Azimuth(da)(curtail) | OptRev_Azimuth(rt)(curtail) | OptCF_Tilt(da)(mustrun) | OptRev_Tilt(da)(mustrun) | OptRev_Tilt(rt)(mustrun) | OptRev_Tilt(da)(curtail) | OptRev_Tilt(rt)(curtail) | |
|---|---|---|---|---|---|---|---|---|---|---|
| count | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 |
| mean | 184.613530 | 226.312989 | 235.799180 | 225.792479 | 232.223856 | 31.610307 | 38.497415 | 43.217181 | 38.182560 | 41.462049 |
| std | 6.331337 | 4.734833 | 4.071666 | 4.426137 | 3.020238 | 1.174554 | 2.353140 | 2.082641 | 2.079906 | 1.433022 |
| min | 168.368400 | 208.259400 | 217.818500 | 208.126500 | 220.084200 | 28.692800 | 33.719100 | 36.953700 | 33.660700 | 36.362300 |
| 2.5% | 174.967700 | 216.834040 | 228.288700 | 216.572060 | 224.933360 | 29.244100 | 34.734680 | 39.646380 | 34.612200 | 38.203560 |
| 25% | 179.521200 | 223.327900 | 233.398800 | 222.908900 | 230.699200 | 30.914800 | 36.984000 | 42.116600 | 36.800200 | 40.743900 |
| 50% | 182.658700 | 225.990800 | 235.445400 | 225.556900 | 232.543000 | 31.662200 | 38.299300 | 42.938400 | 38.111600 | 41.635800 |
| 75% | 189.929100 | 229.231100 | 238.293600 | 228.674800 | 234.236400 | 32.408300 | 39.873100 | 44.184700 | 39.579600 | 42.374400 |
| 97.5% | 196.117200 | 236.494540 | 244.089680 | 234.468600 | 237.494700 | 33.732000 | 43.654320 | 47.713800 | 42.293100 | 43.948660 |
| max | 200.324200 | 252.511100 | 258.640800 | 238.814700 | 240.760500 | 35.208800 | 53.609600 | 57.927100 | 46.326700 | 45.690200 |
median
| OptCF_Azimuth(da)(mustrun) | OptRev_Azimuth(da)(mustrun) | OptRev_Azimuth(rt)(mustrun) | OptRev_Azimuth(da)(curtail) | OptRev_Azimuth(rt)(curtail) | OptCF_Tilt(da)(mustrun) | OptRev_Tilt(da)(mustrun) | OptRev_Tilt(rt)(mustrun) | OptRev_Tilt(da)(curtail) | OptRev_Tilt(rt)(curtail) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2010 | 182.85950 | NaN | NaN | 180.02020 | 179.97350 | 178.9469 | NaN | 189.09155 | NaN | NaN | 185.20700 | 186.38850 | 185.3944 | NaN | 191.8876 | NaN | NaN | 183.34080 | 186.80150 | 183.57140 | NaN | 189.08495 | NaN | NaN | 185.20700 | 186.38850 | 185.39865 | NaN | 191.4729 | NaN | NaN | 183.13830 | 186.97695 | 183.59200 | NaN | 31.68270 | NaN | NaN | 33.9581 | 35.40270 | 34.2143 | NaN | 32.50395 | NaN | NaN | 30.89860 | 34.18990 | 31.58445 | NaN | 32.99645 | NaN | NaN | 30.88910 | 33.38310 | 30.96415 | NaN | 32.50375 | NaN | NaN | 30.89860 | 34.18990 | 31.5899 | NaN | 32.8645 | NaN | NaN | 30.94230 | 33.37485 | 30.98985 | NaN |
| 2011 | 182.85950 | 183.48340 | 177.10000 | 179.93855 | 179.94565 | 178.9633 | NaN | 190.22680 | 222.96080 | 179.4847 | 184.63325 | 186.09170 | 185.2421 | NaN | 193.4248 | 217.39130 | 178.30520 | 183.51755 | 187.17415 | 185.31665 | NaN | 190.22520 | 222.96270 | 179.48470 | 184.59320 | 186.09170 | 185.24210 | NaN | 192.4291 | 217.17530 | 178.3052 | 183.54135 | 187.07210 | 185.36140 | NaN | 31.68200 | 28.8997 | 36.69660 | 33.9147 | 35.36700 | 34.2117 | NaN | 30.60630 | 27.2647 | 35.9748 | 30.65235 | 33.76565 | 30.47460 | NaN | 32.15330 | 26.35930 | 35.6608 | 30.89440 | 33.39995 | 29.60360 | NaN | 30.60510 | 27.25910 | 35.9748 | 30.65195 | 33.76565 | 30.4746 | NaN | 31.8715 | 26.3096 | 35.6182 | 30.90785 | 33.38760 | 29.59415 | NaN |
| 2012 | 182.85950 | 183.49160 | 177.10000 | 179.93670 | 179.91780 | 179.0359 | NaN | 196.83100 | 210.42100 | 182.1204 | 187.71980 | 186.86535 | 185.5009 | NaN | 198.1356 | 204.87860 | 182.16800 | 184.37420 | 188.62590 | 185.24380 | NaN | 196.79485 | 210.47380 | 182.12040 | 187.57250 | 186.86535 | 185.50090 | NaN | 197.5551 | 204.66100 | 182.1680 | 184.41290 | 188.58420 | 185.24680 | NaN | 31.67945 | 28.8915 | 36.68080 | 33.9198 | 35.38485 | 33.9043 | NaN | 32.82990 | 27.8994 | 36.8646 | 31.16140 | 34.37970 | 31.92840 | NaN | 32.23590 | 28.91650 | 36.0648 | 30.99330 | 33.38650 | 31.43370 | NaN | 32.82950 | 27.89850 | 36.8646 | 31.16140 | 34.37970 | 31.9284 | NaN | 32.1445 | 28.9276 | 36.0648 | 31.01940 | 33.43275 | 31.44710 | NaN |
| 2013 | 182.85950 | 183.48340 | 177.16770 | 179.93215 | 179.91780 | 179.0237 | NaN | 191.41150 | 199.81260 | 179.0987 | 181.78220 | 183.99810 | 184.0627 | NaN | 193.4898 | 199.14450 | 178.59320 | 179.33340 | 187.91875 | 181.24930 | NaN | 191.41150 | 199.81320 | 179.09870 | 181.75645 | 183.99810 | 184.06370 | NaN | 192.8638 | 199.12200 | 178.5932 | 179.22590 | 187.97260 | 181.24890 | NaN | 31.67680 | 28.8997 | 36.64780 | 33.9096 | 35.36700 | 33.9444 | NaN | 30.84760 | 26.7706 | 41.5016 | 32.06795 | 36.66745 | 31.65360 | NaN | 31.82680 | 27.77040 | 40.5596 | 31.69430 | 35.39440 | 31.30980 | NaN | 30.85450 | 26.77720 | 41.5016 | 32.06795 | 36.66745 | 31.6555 | NaN | 31.6921 | 27.7723 | 40.5596 | 31.96320 | 35.56490 | 31.31425 | NaN |
| 2014 | 182.70765 | 183.48340 | 177.10920 | 179.51770 | 179.94565 | 179.0239 | NaN | 196.28360 | 196.34470 | 177.7055 | 182.22825 | 180.56500 | 178.7690 | NaN | 202.8121 | 191.54400 | 178.20565 | 180.79725 | 180.78495 | 178.14510 | NaN | 196.28735 | 196.34690 | 177.70550 | 182.16375 | 180.56500 | 178.77125 | NaN | 201.2243 | 191.53530 | 178.1628 | 180.58315 | 180.74145 | 178.14530 | NaN | 31.67520 | 28.9181 | 36.64360 | 31.9505 | 35.36700 | 33.9415 | NaN | 31.28520 | 27.6948 | 44.0570 | 31.82460 | 41.17560 | 35.65420 | NaN | 31.59830 | 28.06940 | 44.3025 | 31.37280 | 41.47910 | 35.12470 | NaN | 31.28245 | 27.69750 | 44.0570 | 31.82145 | 41.17560 | 35.6542 | NaN | 31.4045 | 28.0687 | 44.3279 | 31.37105 | 41.48505 | 35.15010 | NaN |
| 2015 | 182.65870 | 183.47325 | 177.11885 | 179.51770 | 179.91780 | 179.0239 | 176.6678 | 201.51140 | 209.39860 | 179.8746 | 186.17940 | 183.08700 | 183.3174 | 189.10480 | 211.9209 | 194.44200 | 184.20400 | 184.39200 | 187.98720 | 182.20400 | 184.5094 | 201.50190 | 209.39860 | 179.87435 | 186.22240 | 183.08700 | 183.31740 | 189.1048 | 209.1826 | 194.49590 | 184.1738 | 184.52025 | 187.85990 | 182.15050 | 184.5436 | 31.67520 | 28.8913 | 36.73465 | 31.9505 | 35.36700 | 33.9766 | 33.8381 | 30.75835 | 27.0415 | 41.9972 | 30.71250 | 37.85290 | 33.28960 | 31.7660 | 32.56090 | 26.57110 | 41.6799 | 30.66025 | 37.92910 | 32.89930 | 31.8318 | 30.75740 | 27.04150 | 41.9970 | 30.70985 | 37.85290 | 33.2934 | 31.7660 | 31.8224 | 26.5674 | 41.6737 | 30.66450 | 37.92370 | 32.88600 | 32.0401 |
| 2016 | 182.65870 | 183.48060 | 177.11780 | 179.31965 | 179.94565 | 179.0576 | 175.6269 | 207.35340 | 201.53365 | 182.6667 | 188.13190 | 186.87060 | 185.8379 | 197.75705 | 217.7250 | 200.52335 | 188.29590 | 186.10000 | 190.67270 | 185.04970 | 209.5976 | 207.22050 | 201.52835 | 182.66670 | 188.06720 | 186.87060 | 185.83790 | 197.7554 | 215.5064 | 200.57165 | 187.7450 | 186.35185 | 190.61370 | 184.97440 | 204.9632 | 31.67520 | 28.8913 | 36.79230 | 31.7825 | 35.36700 | 33.9415 | 33.6213 | 33.26390 | 28.0822 | 38.0253 | 30.71450 | 35.08710 | 32.63190 | 33.7132 | 37.40870 | 28.25435 | 37.7452 | 30.58480 | 35.41170 | 32.40440 | 36.7933 | 33.25390 | 28.08775 | 38.0253 | 30.72355 | 35.08710 | 32.6339 | 33.7013 | 36.6505 | 28.2964 | 37.7410 | 30.75770 | 34.79315 | 32.42080 | 35.9148 |
| 2017 | 182.65870 | 183.47780 | 177.08580 | 179.33225 | 179.94565 | 179.0587 | 178.4129 | 225.99080 | 199.45370 | 181.0400 | 188.64090 | 185.01690 | 184.5971 | 217.38150 | 235.4454 | 199.09790 | 182.82960 | 187.34370 | 185.71100 | 185.55055 | 235.9535 | 225.55690 | 199.45370 | 181.03975 | 188.65640 | 185.01690 | 184.59855 | 216.9094 | 232.5430 | 199.04910 | 182.8080 | 187.28850 | 185.66780 | 185.39975 | 227.8725 | 31.66220 | 28.8911 | 36.78320 | 31.8333 | 35.36700 | 33.9866 | 33.2364 | 38.29930 | 27.2439 | 40.5822 | 30.40280 | 36.21495 | 33.36990 | 36.7391 | 42.93840 | 27.17330 | 41.5447 | 30.42520 | 37.23240 | 33.30000 | 43.1225 | 38.11160 | 27.27030 | 40.5822 | 30.40280 | 36.21495 | 33.3705 | 36.5472 | 41.6358 | 27.2233 | 41.4991 | 30.45320 | 37.11685 | 33.29085 | 38.7322 |
max
| OptCF_Azimuth(da)(mustrun) | OptRev_Azimuth(da)(mustrun) | OptRev_Azimuth(rt)(mustrun) | OptRev_Azimuth(da)(curtail) | OptRev_Azimuth(rt)(curtail) | OptCF_Tilt(da)(mustrun) | OptRev_Tilt(da)(mustrun) | OptRev_Tilt(rt)(mustrun) | OptRev_Tilt(da)(curtail) | OptRev_Tilt(rt)(curtail) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2010 | 200.3242 | NaN | NaN | 186.815 | 185.7652 | 187.0084 | NaN | 203.0964 | NaN | NaN | 194.0741 | 193.3475 | 195.8039 | NaN | 205.0437 | NaN | NaN | 189.2190 | 196.8200 | 192.3163 | NaN | 203.0965 | NaN | NaN | 194.0741 | 193.3475 | 195.8039 | NaN | 204.8800 | NaN | NaN | 189.2183 | 196.7783 | 192.3165 | NaN | 35.2088 | NaN | NaN | 39.6242 | 36.6046 | 36.8202 | NaN | 35.4261 | NaN | NaN | 38.5893 | 36.6930 | 35.5558 | NaN | 52.6320 | NaN | NaN | 37.5111 | 35.1908 | 36.2611 | NaN | 35.4244 | NaN | NaN | 38.5835 | 36.6930 | 35.5558 | NaN | 42.5957 | NaN | NaN | 37.4994 | 35.2366 | 36.3277 | NaN |
| 2011 | 200.3242 | 193.924 | 182.2855 | 186.815 | 185.7652 | 187.0084 | NaN | 205.2315 | 237.6268 | 185.3909 | 193.2689 | 194.5615 | 193.5793 | NaN | 207.1296 | 230.4955 | 184.0411 | 191.2672 | 194.9198 | 195.1934 | NaN | 205.2284 | 237.6268 | 185.3909 | 193.2136 | 194.5615 | 193.5793 | NaN | 206.6391 | 230.2195 | 184.0411 | 190.7246 | 194.8559 | 195.1934 | NaN | 35.2088 | 31.572 | 39.6473 | 39.6242 | 36.6046 | 36.8202 | NaN | 37.5061 | 34.5644 | 39.7165 | 37.9905 | 35.3708 | 33.7044 | NaN | 38.6189 | 29.9489 | 39.3725 | 37.3453 | 35.2511 | 32.7081 | NaN | 37.3298 | 33.1280 | 39.7165 | 37.9904 | 35.3708 | 33.7044 | NaN | 36.6292 | 29.9409 | 39.3725 | 37.4805 | 35.1135 | 32.5442 | NaN |
| 2012 | 200.3242 | 193.924 | 182.2855 | 186.815 | 185.7652 | 187.0084 | NaN | 214.7674 | 250.4336 | 187.4748 | 195.2608 | 202.4769 | 195.1589 | NaN | 240.5607 | 243.1014 | 192.0146 | 195.9897 | 200.4531 | 195.9943 | NaN | 214.7674 | 250.4336 | 187.4748 | 195.2608 | 202.4769 | 195.1589 | NaN | 240.5353 | 243.0977 | 192.0146 | 190.8303 | 200.4265 | 195.6853 | NaN | 35.2088 | 31.572 | 39.6473 | 39.6242 | 36.6046 | 36.8202 | NaN | 37.6982 | 33.4546 | 40.3640 | 37.5766 | 37.2344 | 35.3047 | NaN | 41.4229 | 39.0149 | 39.3825 | 36.7474 | 35.4960 | 35.8552 | NaN | 37.5482 | 33.4858 | 40.3640 | 37.5766 | 37.2344 | 35.3047 | NaN | 40.8937 | 39.0145 | 39.3822 | 36.6366 | 35.4857 | 35.8552 | NaN |
| 2013 | 200.3242 | 193.924 | 183.5227 | 186.815 | 185.7652 | 187.0084 | NaN | 206.9637 | 227.3251 | 184.7187 | 189.2500 | 193.9252 | 196.1463 | NaN | 208.7176 | 227.1377 | 185.4995 | 188.7444 | 199.6329 | 207.9853 | NaN | 206.9637 | 227.3251 | 184.7187 | 189.0939 | 193.9252 | 196.1463 | NaN | 207.9256 | 227.1282 | 185.4995 | 188.5506 | 199.6361 | 207.9853 | NaN | 35.2088 | 31.572 | 39.6473 | 39.6242 | 36.6046 | 36.8202 | NaN | 35.0620 | 29.7353 | 45.2676 | 40.0781 | 39.8188 | 36.4184 | NaN | 35.9119 | 31.4930 | 44.7731 | 40.6843 | 38.8753 | 37.1026 | NaN | 35.0610 | 29.7353 | 45.2676 | 40.0781 | 39.8188 | 36.4184 | NaN | 35.6612 | 31.5218 | 44.7769 | 40.5370 | 38.8533 | 37.1026 | NaN |
| 2014 | 200.3242 | 193.924 | 183.5227 | 186.815 | 185.7652 | 187.0084 | NaN | 214.3079 | 209.5750 | 182.2823 | 205.7640 | 187.6079 | 188.4843 | NaN | 224.9102 | 211.7599 | 182.3004 | 201.3445 | 190.5220 | 204.2635 | NaN | 214.3078 | 209.5750 | 182.2823 | 205.7177 | 187.6079 | 188.4843 | NaN | 224.1930 | 211.7739 | 182.2620 | 201.1911 | 190.4667 | 204.2635 | NaN | 35.2088 | 31.572 | 39.6473 | 39.6242 | 36.6046 | 36.8202 | NaN | 34.7048 | 32.7132 | 47.1449 | 40.3402 | 43.2937 | 43.6029 | NaN | 35.3231 | 35.0470 | 46.8744 | 39.9205 | 44.6806 | 43.7675 | NaN | 34.7048 | 32.7243 | 47.1449 | 40.3402 | 43.2937 | 43.6029 | NaN | 34.6671 | 35.0438 | 46.9077 | 39.6111 | 44.6586 | 43.6766 | NaN |
| 2015 | 200.3242 | 193.924 | 183.5227 | 186.815 | 185.7652 | 187.0084 | 195.0602 | 236.0143 | 221.9458 | 191.6743 | 197.2449 | 193.4627 | 194.0264 | 212.0025 | 239.3812 | 238.9267 | 189.9292 | 202.3802 | 201.2787 | 202.2951 | 207.0136 | 236.0143 | 221.9458 | 191.6743 | 197.2449 | 193.4627 | 194.0264 | 212.0025 | 238.7308 | 238.9868 | 190.0004 | 202.3568 | 201.2246 | 202.0031 | 205.8385 | 35.2088 | 31.572 | 39.6473 | 39.6242 | 36.6046 | 36.8202 | 38.3654 | 35.3977 | 30.9044 | 44.1511 | 39.2335 | 40.7012 | 39.3176 | 36.0870 | 39.0796 | 35.3221 | 44.3236 | 39.2039 | 40.7004 | 41.1574 | 37.1280 | 34.9652 | 30.9075 | 44.1511 | 39.2335 | 40.7012 | 39.3176 | 36.0870 | 36.4279 | 35.2816 | 44.3412 | 39.2168 | 40.6639 | 41.1215 | 37.0679 |
| 2016 | 200.3242 | 193.924 | 183.5227 | 186.815 | 185.7652 | 187.0084 | 195.0602 | 219.2098 | 221.9569 | 189.9027 | 197.0652 | 200.1599 | 196.2292 | 217.6408 | 239.5015 | 226.9745 | 198.7042 | 196.3130 | 209.5217 | 195.1880 | 229.0691 | 218.9425 | 220.9647 | 189.9027 | 197.0652 | 200.1599 | 196.2292 | 217.6407 | 229.3088 | 226.6087 | 196.5223 | 196.2718 | 208.8442 | 195.1857 | 226.6412 | 35.2088 | 31.572 | 39.6473 | 39.6242 | 36.6046 | 36.8202 | 38.3654 | 36.7116 | 32.8025 | 40.6591 | 39.0716 | 37.2069 | 37.7067 | 38.0778 | 42.9403 | 34.4899 | 40.1544 | 39.0292 | 38.5277 | 38.8492 | 41.7094 | 36.7116 | 32.8025 | 40.6591 | 39.0679 | 37.2069 | 37.6136 | 38.0778 | 41.4139 | 34.3587 | 40.1620 | 38.8385 | 38.2816 | 38.3669 | 40.7555 |
| 2017 | 200.3242 | 193.924 | 183.5227 | 186.815 | 185.7652 | 187.0084 | 196.7155 | 252.5111 | 215.9665 | 189.9117 | 198.9783 | 192.5879 | 195.7794 | 234.1381 | 258.6408 | 216.2665 | 192.2369 | 196.8801 | 200.1029 | 205.3014 | 268.0486 | 238.8147 | 215.9666 | 189.9117 | 198.9783 | 192.5879 | 195.7794 | 233.9650 | 240.7605 | 216.1467 | 192.2021 | 196.8801 | 200.0905 | 205.0568 | 238.2724 | 35.2088 | 31.572 | 39.6473 | 39.6242 | 36.6046 | 36.8202 | 38.3654 | 53.6096 | 34.5111 | 44.4107 | 38.8008 | 38.7876 | 38.3568 | 45.0457 | 57.9271 | 41.2006 | 45.4576 | 39.8040 | 41.1456 | 39.8834 | 48.7947 | 46.3267 | 33.8992 | 44.2167 | 38.8237 | 38.7876 | 38.3568 | 44.9395 | 45.6902 | 36.3601 | 44.7203 | 38.9162 | 39.9085 | 39.7439 | 44.5515 |
OptCF_Azimuth(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 184.784422 | 184.780772 | 184.743928 | 184.668910 | 184.625658 | 184.621536 | 184.618062 | 184.613530 | 183.728347 | 183.733619 | 183.729803 | 183.726376 | 183.716132 | 183.716984 | 183.717720 | NaN | 177.280703 | 177.283441 | 177.355053 | 177.322469 | 177.327681 | 177.314551 | 177.269676 | 180.133160 | 180.177276 | 180.180880 | 180.175165 | 179.569879 | 179.580764 | 179.508782 | 179.534263 | 179.494221 | 179.507948 | 179.523979 | 179.544076 | 179.552625 | 179.549622 | 179.557790 | 179.557790 | 178.987051 | 178.997281 | 179.108577 | 179.101311 | 179.104540 | 179.099351 | 179.115320 | 179.120117 | 177.364601 | 176.287991 | 178.452795 |
| std | 6.329045 | 6.331391 | 6.321225 | 6.318535 | 6.319824 | 6.318641 | 6.319364 | 6.331337 | 2.449782 | 2.450419 | 2.450925 | 2.453753 | 2.459618 | 2.459784 | 2.464699 | NaN | 1.657133 | 1.630715 | 1.637373 | 1.640075 | 1.604641 | 1.586874 | 1.557755 | 2.291087 | 2.295743 | 2.305142 | 2.289264 | 2.720174 | 2.727798 | 2.759486 | 2.724459 | 2.164374 | 2.174284 | 2.147454 | 2.153474 | 2.151902 | 2.150333 | 2.154622 | 2.154622 | 1.765614 | 1.768602 | 1.883145 | 1.889437 | 1.878194 | 1.875746 | 1.884853 | 1.882209 | 5.421593 | 4.388795 | 5.083917 |
| min | 168.368400 | 168.368400 | 168.368400 | 168.368400 | 168.368400 | 168.368400 | 168.368400 | 168.368400 | 175.637800 | 175.637800 | 175.637800 | 175.637800 | 175.637800 | 175.637800 | 175.637800 | NaN | 173.379800 | 173.379800 | 173.379800 | 173.379800 | 173.379800 | 173.379800 | 173.379800 | 175.252100 | 175.252100 | 175.252100 | 175.252100 | 172.457000 | 172.457000 | 171.422300 | 171.422300 | 172.556200 | 172.556200 | 172.556200 | 172.556200 | 172.556200 | 172.556200 | 172.556200 | 172.556200 | 173.858100 | 173.858100 | 173.858100 | 173.858100 | 173.858100 | 173.858100 | 173.858100 | 173.858100 | 164.815200 | 164.815200 | 164.815200 |
| 2.5% | 174.979003 | 174.981400 | 174.981400 | 174.968385 | 174.967700 | 174.967700 | 174.967700 | 174.967700 | 178.803600 | 178.812055 | 178.811940 | 178.814720 | 178.803600 | 178.803600 | 178.803600 | NaN | 174.127100 | 174.248910 | 174.348760 | 174.370025 | 174.316275 | 174.319620 | 174.399250 | 175.593985 | 175.633408 | 175.648570 | 175.657668 | 174.290595 | 174.302365 | 174.249400 | 174.249400 | 174.570705 | 174.584755 | 174.601615 | 174.610045 | 174.615665 | 174.617070 | 174.618475 | 174.618475 | 175.777400 | 175.750450 | 175.746000 | 175.746000 | 175.746000 | 175.746000 | 175.746000 | 175.746075 | 168.193500 | 169.035700 | 170.089750 |
| 25% | 179.610800 | 179.610800 | 179.596650 | 179.580900 | 179.568300 | 179.562725 | 179.546000 | 179.521200 | 182.349300 | 182.350400 | 182.350400 | 182.348200 | 182.336100 | 182.336100 | 182.328750 | NaN | 176.178900 | 176.214200 | 176.280900 | 176.234525 | 176.285725 | 176.280900 | 176.234375 | 178.384600 | 178.411800 | 178.411800 | 178.411800 | 177.930250 | 177.938750 | 177.897025 | 177.906700 | 178.083500 | 178.083500 | 178.083500 | 178.083500 | 178.083500 | 178.083500 | 178.083500 | 178.083500 | 177.778300 | 177.778300 | 177.816400 | 177.792900 | 177.817500 | 177.817500 | 177.817500 | 177.820250 | 174.252600 | 174.216050 | 175.198400 |
| 50% | 182.859500 | 182.859500 | 182.859500 | 182.859500 | 182.707650 | 182.658700 | 182.658700 | 182.658700 | 183.483400 | 183.491600 | 183.483400 | 183.483400 | 183.473250 | 183.480600 | 183.477800 | NaN | 177.100000 | 177.100000 | 177.167700 | 177.109200 | 177.118850 | 177.117800 | 177.085800 | 180.020200 | 179.938550 | 179.936700 | 179.932150 | 179.517700 | 179.517700 | 179.319650 | 179.332250 | 179.973500 | 179.945650 | 179.917800 | 179.917800 | 179.945650 | 179.917800 | 179.945650 | 179.945650 | 178.946900 | 178.963300 | 179.035900 | 179.023700 | 179.023900 | 179.023900 | 179.057600 | 179.058700 | 176.667800 | 175.626900 | 178.412900 |
| 75% | 190.023300 | 190.023300 | 189.937800 | 189.929100 | 189.882350 | 189.843050 | 189.823400 | 189.929100 | 185.022500 | 185.027800 | 185.027800 | 185.022500 | 185.022500 | 185.022500 | 185.034250 | NaN | 178.322700 | 178.322700 | 178.350600 | 178.341450 | 178.350600 | 178.350600 | 178.322700 | 181.437250 | 181.614875 | 181.592400 | 181.501725 | 181.338800 | 181.338800 | 181.338800 | 181.278475 | 180.576500 | 180.576500 | 180.576500 | 180.576500 | 180.576500 | 180.576500 | 180.576500 | 180.576500 | 180.073600 | 180.094400 | 180.208600 | 180.204800 | 180.204800 | 180.186300 | 180.220000 | 180.213925 | 178.677300 | 177.363700 | 180.064000 |
| 97.5% | 196.211000 | 196.211000 | 196.211000 | 196.206310 | 196.133615 | 196.128925 | 196.126580 | 196.117200 | 189.176300 | 189.171445 | 189.170735 | 189.170380 | 189.170202 | 189.170202 | 189.171445 | NaN | 181.359310 | 181.184100 | 181.077700 | 181.016900 | 180.977275 | 180.917730 | 180.480200 | 185.031300 | 185.031300 | 185.041230 | 185.033782 | 184.948165 | 184.945755 | 184.926475 | 184.908400 | 184.351472 | 184.322197 | 184.287068 | 184.295965 | 184.287905 | 184.285890 | 184.283875 | 184.283875 | 182.651575 | 182.582850 | 183.351800 | 183.351800 | 183.345425 | 183.342300 | 183.351800 | 183.351800 | 190.868700 | 189.587600 | 190.868700 |
| max | 200.324200 | 200.324200 | 200.324200 | 200.324200 | 200.324200 | 200.324200 | 200.324200 | 200.324200 | 193.924000 | 193.924000 | 193.924000 | 193.924000 | 193.924000 | 193.924000 | 193.924000 | NaN | 182.285500 | 182.285500 | 183.522700 | 183.522700 | 183.522700 | 183.522700 | 183.522700 | 186.815000 | 186.815000 | 186.815000 | 186.815000 | 186.815000 | 186.815000 | 186.815000 | 186.815000 | 185.765200 | 185.765200 | 185.765200 | 185.765200 | 185.765200 | 185.765200 | 185.765200 | 185.765200 | 187.008400 | 187.008400 | 187.008400 | 187.008400 | 187.008400 | 187.008400 | 187.008400 | 187.008400 | 195.060200 | 195.060200 | 196.715500 |
OptRev_Azimuth(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.00000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 190.172515 | 191.435390 | 197.133760 | 192.353465 | 196.765930 | 201.921176 | 207.431811 | 226.312989 | 222.554475 | 210.921695 | 200.569615 | 196.711416 | 209.389675 | 201.817638 | 199.465265 | NaN | 179.864122 | 182.207962 | 179.255217 | 177.765261 | 180.021849 | 182.86952 | 181.343592 | 185.330886 | 184.672530 | 187.607686 | 182.006323 | 183.526219 | 186.222462 | 188.310853 | 188.779419 | 185.571790 | 185.499930 | 188.097716 | 184.466650 | 180.420395 | 183.476743 | 188.001723 | 185.197625 | 185.388161 | 185.336810 | 185.564738 | 184.234134 | 178.981831 | 183.591682 | 186.043262 | 184.750200 | 189.593855 | 197.073607 | 217.224124 |
| std | 4.733824 | 5.296078 | 5.460843 | 5.080868 | 5.749565 | 7.318307 | 5.788595 | 4.734833 | 3.751428 | 5.677455 | 3.908703 | 3.668117 | 3.258881 | 3.113769 | 4.160837 | NaN | 1.889165 | 1.952703 | 1.416455 | 1.441744 | 1.966422 | 1.93331 | 1.808426 | 2.270712 | 2.269169 | 2.416951 | 2.399258 | 5.712800 | 3.634844 | 2.467944 | 3.258372 | 3.346139 | 3.724804 | 4.438360 | 3.468057 | 2.722997 | 2.637872 | 3.849073 | 2.523436 | 2.026393 | 2.197698 | 1.984219 | 2.113156 | 2.209310 | 2.268179 | 2.302082 | 2.216620 | 6.775977 | 6.903429 | 7.897510 |
| min | 175.340900 | 176.405300 | 180.605700 | 173.960700 | 171.891400 | 182.139600 | 190.686300 | 208.259400 | 192.539900 | 119.510100 | 190.768900 | 179.893800 | 197.914300 | 192.973700 | 177.030800 | NaN | 175.968400 | 177.570100 | 175.728300 | 173.078000 | 175.108100 | 178.48690 | 177.319000 | 178.818400 | 178.562400 | 181.727500 | 176.360900 | 173.142700 | 176.491600 | 180.670200 | 180.997000 | 176.937500 | 176.476700 | 180.672700 | 175.970400 | 171.823000 | 175.978600 | 180.183400 | 177.912700 | 178.997400 | 178.424000 | 178.882300 | 177.815000 | 172.215900 | 176.995500 | 179.949900 | 178.741100 | 168.892800 | 169.045900 | 170.189200 |
| 2.5% | 183.127505 | 183.450000 | 187.849843 | 183.998045 | 187.632712 | 191.087187 | 196.137100 | 216.834040 | 214.761765 | 201.199075 | 194.341000 | 189.987220 | 203.361928 | 196.454435 | 191.912025 | NaN | 176.813910 | 178.757700 | 177.031560 | 175.326797 | 177.342700 | 179.29067 | 178.345200 | 180.510080 | 180.378213 | 182.343500 | 177.834052 | 176.617545 | 179.618607 | 183.808275 | 183.941798 | 178.320975 | 178.224330 | 181.729798 | 178.147600 | 173.913990 | 178.491765 | 182.171400 | 179.341913 | 181.367000 | 181.314300 | 181.938040 | 180.551900 | 175.132375 | 179.707640 | 182.005650 | 180.728338 | 178.491380 | 181.781422 | 194.593000 |
| 25% | 186.435775 | 187.331400 | 192.949150 | 188.231750 | 191.762475 | 196.647400 | 202.757100 | 223.327900 | 220.762050 | 208.724000 | 197.909450 | 194.192100 | 207.082925 | 199.929350 | 196.624650 | NaN | 178.733400 | 180.832000 | 178.270300 | 176.703175 | 178.819250 | 181.54220 | 180.203925 | 183.832300 | 183.357400 | 186.409900 | 180.346650 | 179.684950 | 183.615700 | 186.464350 | 186.107700 | 183.439925 | 182.773200 | 185.665700 | 182.471525 | 178.850425 | 182.367900 | 186.041425 | 184.111000 | 184.045900 | 183.893350 | 184.255650 | 182.790275 | 177.488975 | 182.045700 | 184.423300 | 183.326325 | 185.874400 | 194.587025 | 214.625600 |
| 50% | 189.091550 | 190.226800 | 196.831000 | 191.411500 | 196.283600 | 201.511400 | 207.353400 | 225.990800 | 222.960800 | 210.421000 | 199.812600 | 196.344700 | 209.398600 | 201.533650 | 199.453700 | NaN | 179.484700 | 182.120400 | 179.098700 | 177.705500 | 179.874600 | 182.66670 | 181.040000 | 185.207000 | 184.633250 | 187.719800 | 181.782200 | 182.228250 | 186.179400 | 188.131900 | 188.640900 | 186.388500 | 186.091700 | 186.865350 | 183.998100 | 180.565000 | 183.087000 | 186.870600 | 185.016900 | 185.394400 | 185.242100 | 185.500900 | 184.062700 | 178.769000 | 183.317400 | 185.837900 | 184.597100 | 189.104800 | 197.757050 | 217.381500 |
| 75% | 193.507125 | 195.459200 | 200.599075 | 196.207200 | 201.428675 | 206.602950 | 212.047200 | 229.231100 | 224.934050 | 213.054850 | 202.947950 | 199.220400 | 211.806100 | 203.746700 | 202.335100 | NaN | 180.847500 | 183.353950 | 179.997400 | 178.640525 | 180.758400 | 184.00490 | 182.318200 | 186.598750 | 185.568800 | 188.743400 | 183.499700 | 185.616600 | 188.735500 | 189.675775 | 190.856350 | 187.730400 | 186.814500 | 188.309100 | 185.115575 | 181.883600 | 185.073400 | 188.814825 | 186.673950 | 186.677225 | 186.736175 | 186.781350 | 185.488850 | 180.324075 | 184.905300 | 187.517000 | 185.885700 | 191.810400 | 199.863100 | 220.414100 |
| 97.5% | 199.657700 | 201.808600 | 208.262428 | 202.387590 | 207.194850 | 216.846275 | 217.065800 | 236.494540 | 228.438245 | 221.701710 | 208.028230 | 203.873100 | 215.098478 | 208.162045 | 206.626935 | NaN | 184.819380 | 186.430540 | 182.485900 | 180.880410 | 183.641800 | 187.27300 | 186.047500 | 190.246720 | 190.088628 | 192.961040 | 187.768100 | 201.029195 | 193.435400 | 193.506075 | 195.945378 | 191.205827 | 192.851587 | 198.140497 | 191.815540 | 185.757200 | 188.576460 | 197.989413 | 190.095413 | 189.503398 | 190.007938 | 190.133555 | 188.965600 | 183.873000 | 188.852260 | 191.069500 | 190.000837 | 206.197340 | 212.630538 | 231.369250 |
| max | 203.096400 | 205.231500 | 214.767400 | 206.963700 | 214.307900 | 236.014300 | 219.209800 | 252.511100 | 237.626800 | 250.433600 | 227.325100 | 209.575000 | 221.945800 | 221.956900 | 215.966500 | NaN | 185.390900 | 187.474800 | 184.718700 | 182.282300 | 191.674300 | 189.90270 | 189.911700 | 194.074100 | 193.268900 | 195.260800 | 189.250000 | 205.764000 | 197.244900 | 197.065200 | 198.978300 | 193.347500 | 194.561500 | 202.476900 | 193.925200 | 187.607900 | 193.462700 | 200.159900 | 192.587900 | 195.803900 | 193.579300 | 195.158900 | 196.146300 | 188.484300 | 194.026400 | 196.229200 | 195.779400 | 212.002500 | 217.640800 | 234.138100 |
OptRev_Azimuth(rt)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 192.863685 | 194.056246 | 200.428547 | 194.294435 | 203.275124 | 211.631095 | 218.677032 | 235.799180 | 217.333955 | 205.696552 | 199.593010 | 191.666016 | 194.832649 | 200.717598 | 199.032793 | NaN | 178.604776 | 182.163869 | 178.761399 | 178.252055 | 184.248032 | 187.989698 | 182.991861 | 183.187047 | 183.538641 | 184.328933 | 179.941275 | 181.437930 | 184.670105 | 186.247951 | 187.594708 | 186.751352 | 186.833574 | 188.707529 | 188.302579 | 180.712431 | 188.172034 | 192.701954 | 186.922429 | 183.622990 | 185.450960 | 185.392699 | 182.266469 | 178.487500 | 182.594101 | 185.266991 | 185.806670 | 184.882650 | 209.014944 | 230.696599 |
| std | 4.590793 | 4.802732 | 11.877469 | 6.059847 | 6.269247 | 6.978609 | 6.483776 | 4.071666 | 5.172406 | 5.766333 | 5.285122 | 3.821839 | 4.098195 | 4.096775 | 5.756059 | NaN | 1.972163 | 2.126328 | 1.778669 | 1.464278 | 2.013315 | 2.781292 | 2.320403 | 2.346776 | 2.572994 | 2.771527 | 2.782634 | 5.214247 | 5.408519 | 3.548423 | 3.348196 | 3.729056 | 3.381006 | 4.338021 | 3.401446 | 3.664179 | 3.682295 | 5.711433 | 4.043769 | 2.355872 | 3.043629 | 2.170106 | 5.174045 | 3.098406 | 2.735840 | 2.573385 | 2.498767 | 4.916169 | 10.748611 | 15.592437 |
| min | 177.838700 | 174.238700 | 160.269700 | 175.642200 | 174.474400 | 183.909000 | 197.535800 | 217.818500 | 186.845700 | 189.777000 | 176.206200 | 172.358300 | 179.346200 | 184.346300 | 158.955300 | NaN | 174.413500 | 177.281300 | 174.167300 | 173.683900 | 179.170600 | 178.335900 | 175.220800 | 176.843900 | 177.626100 | 178.388100 | 174.102900 | 170.763300 | 172.170000 | 173.609300 | 177.487100 | 176.844400 | 179.660000 | 178.270600 | 180.112900 | 168.589600 | 178.805500 | 180.667500 | 177.894700 | 172.272000 | 175.050100 | 178.276900 | 171.229800 | 170.269900 | 174.195800 | 171.486800 | 173.370500 | 168.167600 | 172.983400 | 179.404300 |
| 2.5% | 185.976873 | 184.407000 | 173.023383 | 184.825530 | 193.033900 | 199.083225 | 207.528640 | 228.288700 | 206.080935 | 197.907700 | 188.991965 | 184.720100 | 187.992218 | 193.554688 | 186.773965 | NaN | 175.009500 | 178.253525 | 175.779600 | 175.615000 | 180.643500 | 182.363550 | 178.668138 | 178.667235 | 179.134165 | 179.760290 | 175.489352 | 174.135993 | 175.619878 | 179.701000 | 182.193308 | 178.688287 | 181.399950 | 180.635148 | 181.849250 | 173.245780 | 181.659130 | 184.065700 | 180.473625 | 179.345500 | 179.695437 | 181.670290 | 177.063600 | 173.498975 | 178.290740 | 180.736200 | 181.676325 | 175.900720 | 186.512500 | 198.372700 |
| 25% | 189.324400 | 191.075300 | 194.474775 | 189.635700 | 198.133750 | 206.580875 | 213.594200 | 233.398800 | 214.112500 | 202.755100 | 196.772650 | 189.416900 | 192.223625 | 198.738400 | 195.745500 | NaN | 177.290300 | 180.749350 | 177.578300 | 177.281350 | 182.910625 | 186.242600 | 181.359375 | 181.614700 | 181.936850 | 182.453400 | 178.054275 | 177.389350 | 180.117800 | 184.001625 | 185.169825 | 184.218725 | 184.205150 | 186.100225 | 186.517975 | 178.907350 | 185.663900 | 189.773925 | 185.209500 | 182.007950 | 183.431200 | 183.960050 | 179.530325 | 176.275575 | 180.786400 | 183.591900 | 184.164100 | 181.758100 | 201.823400 | 217.037200 |
| 50% | 191.887600 | 193.424800 | 198.135600 | 193.489800 | 202.812100 | 211.920900 | 217.725000 | 235.445400 | 217.391300 | 204.878600 | 199.144500 | 191.544000 | 194.442000 | 200.523350 | 199.097900 | NaN | 178.305200 | 182.168000 | 178.593200 | 178.205650 | 184.204000 | 188.295900 | 182.829600 | 183.340800 | 183.517550 | 184.374200 | 179.333400 | 180.797250 | 184.392000 | 186.100000 | 187.343700 | 186.801500 | 187.174150 | 188.625900 | 187.918750 | 180.784950 | 187.987200 | 190.672700 | 185.711000 | 183.571400 | 185.316650 | 185.243800 | 181.249300 | 178.145100 | 182.204000 | 185.049700 | 185.550550 | 184.509400 | 209.597600 | 235.953500 |
| 75% | 196.432475 | 197.159400 | 206.819150 | 198.347500 | 207.884600 | 216.633000 | 224.409900 | 238.293600 | 221.167850 | 207.380000 | 202.497650 | 193.946400 | 197.088575 | 202.273125 | 202.939550 | NaN | 179.702900 | 183.412250 | 179.495100 | 179.062600 | 185.562575 | 189.858700 | 184.534850 | 184.662950 | 185.090450 | 186.138800 | 181.788600 | 183.824450 | 188.900775 | 188.407225 | 190.182125 | 189.450300 | 189.226900 | 190.765950 | 189.879125 | 181.895675 | 190.638600 | 195.141175 | 188.411700 | 185.126375 | 187.461350 | 186.596150 | 183.073900 | 180.227850 | 183.922400 | 186.770000 | 187.172000 | 186.879800 | 217.616600 | 244.054100 |
| 97.5% | 201.661900 | 202.701900 | 231.479788 | 206.104195 | 215.343800 | 222.698625 | 230.520460 | 244.089680 | 226.163450 | 219.122300 | 210.307100 | 199.498680 | 203.609355 | 211.971107 | 208.459400 | NaN | 183.127020 | 186.729470 | 183.166100 | 181.433030 | 188.549750 | 192.381100 | 187.517025 | 187.897475 | 189.241122 | 189.246370 | 185.792777 | 195.674560 | 195.832800 | 193.386050 | 193.949280 | 192.506362 | 192.794670 | 197.640595 | 195.157545 | 189.779600 | 195.090960 | 205.927488 | 196.266938 | 188.538483 | 191.418325 | 190.397235 | 201.984610 | 185.373075 | 189.380000 | 191.239650 | 191.669625 | 196.713160 | 223.675880 | 250.556600 |
| max | 205.043700 | 207.129600 | 240.560700 | 208.717600 | 224.910200 | 239.381200 | 239.501500 | 258.640800 | 230.495500 | 243.101400 | 227.137700 | 211.759900 | 238.926700 | 226.974500 | 216.266500 | NaN | 184.041100 | 192.014600 | 185.499500 | 182.300400 | 189.929200 | 198.704200 | 192.236900 | 189.219000 | 191.267200 | 195.989700 | 188.744400 | 201.344500 | 202.380200 | 196.313000 | 196.880100 | 196.820000 | 194.919800 | 200.453100 | 199.632900 | 190.522000 | 201.278700 | 209.521700 | 200.102900 | 192.316300 | 195.193400 | 195.994300 | 207.985300 | 204.263500 | 202.295100 | 195.188000 | 205.301400 | 207.013600 | 229.069100 | 268.048600 |
OptRev_Azimuth(da)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 190.170495 | 191.459930 | 197.106450 | 192.352177 | 196.769575 | 201.893079 | 207.359627 | 225.792479 | 222.559863 | 210.939877 | 200.568184 | 196.713805 | 209.391117 | 201.807599 | 199.469483 | NaN | 179.864121 | 182.208520 | 179.255464 | 177.765300 | 180.021670 | 182.869323 | 181.342236 | 185.323825 | 184.670352 | 187.613206 | 181.988803 | 183.528810 | 186.238300 | 188.304485 | 188.795199 | 185.571888 | 185.499930 | 188.098264 | 184.466369 | 180.420395 | 183.477390 | 188.002034 | 185.197515 | 185.391518 | 185.339007 | 185.566772 | 184.234280 | 178.983590 | 183.587144 | 186.044594 | 184.751667 | 189.593781 | 197.066711 | 216.784930 |
| std | 4.734683 | 5.270091 | 5.437742 | 5.079368 | 5.743449 | 7.296453 | 5.733881 | 4.426137 | 3.755298 | 5.498849 | 3.908129 | 3.666889 | 3.256895 | 3.077095 | 4.130284 | NaN | 1.889166 | 1.952892 | 1.416390 | 1.441690 | 1.966365 | 1.933056 | 1.805898 | 2.274120 | 2.268193 | 2.374925 | 2.390748 | 5.716153 | 3.604148 | 2.468537 | 3.251710 | 3.345958 | 3.724804 | 4.437623 | 3.467201 | 2.722997 | 2.637987 | 3.848915 | 2.523593 | 2.024773 | 2.193047 | 1.980953 | 2.112494 | 2.207787 | 2.261124 | 2.302735 | 2.215346 | 6.775994 | 6.896367 | 7.843399 |
| min | 175.337700 | 176.375100 | 180.592100 | 173.960700 | 171.890200 | 182.139600 | 190.671200 | 208.126500 | 192.530000 | 138.981700 | 190.768900 | 179.893800 | 197.914300 | 192.973700 | 182.854100 | NaN | 175.968400 | 177.570100 | 175.728300 | 173.078000 | 175.108100 | 178.486900 | 177.319000 | 178.818400 | 178.562400 | 181.727500 | 176.360900 | 173.142700 | 176.491600 | 180.670200 | 180.997300 | 176.937500 | 176.476700 | 180.672700 | 175.970400 | 171.823000 | 175.978600 | 180.183400 | 177.912700 | 178.997400 | 178.424000 | 178.882300 | 177.815000 | 172.215900 | 176.995500 | 179.949900 | 178.741100 | 168.892800 | 169.046200 | 170.337300 |
| 2.5% | 183.150165 | 183.544600 | 187.544403 | 183.997870 | 187.631392 | 191.087975 | 196.262370 | 216.572060 | 214.756390 | 200.899145 | 194.341000 | 189.987220 | 203.361928 | 196.609358 | 191.924510 | NaN | 176.813910 | 178.757700 | 177.031560 | 175.326797 | 177.342600 | 179.290670 | 178.382125 | 180.510080 | 180.378213 | 182.412390 | 177.834052 | 176.587267 | 179.618607 | 183.827650 | 183.941798 | 178.320975 | 178.224330 | 181.729798 | 178.147600 | 173.913990 | 178.491765 | 182.171400 | 179.341913 | 181.367000 | 181.314300 | 181.957645 | 180.551900 | 175.139900 | 179.707580 | 182.005650 | 180.746000 | 178.491380 | 181.781422 | 194.569850 |
| 25% | 186.434300 | 187.353900 | 192.995400 | 188.230850 | 191.763050 | 196.644000 | 202.718500 | 222.908900 | 220.763050 | 208.724000 | 197.909450 | 194.190600 | 207.082925 | 199.953025 | 196.618150 | NaN | 178.733400 | 180.832000 | 178.270300 | 176.703500 | 178.819250 | 181.542200 | 180.203925 | 183.832300 | 183.357400 | 186.409900 | 180.346600 | 179.657625 | 183.615700 | 186.426875 | 186.111050 | 183.439925 | 182.773200 | 185.665700 | 182.460900 | 178.850425 | 182.367900 | 186.041425 | 184.111000 | 184.055125 | 183.893350 | 184.255650 | 182.790275 | 177.490575 | 182.045700 | 184.423400 | 183.326325 | 185.874400 | 194.596300 | 214.074000 |
| 50% | 189.084950 | 190.225200 | 196.794850 | 191.411500 | 196.287350 | 201.501900 | 207.220500 | 225.556900 | 222.962700 | 210.473800 | 199.813200 | 196.346900 | 209.398600 | 201.528350 | 199.453700 | NaN | 179.484700 | 182.120400 | 179.098700 | 177.705500 | 179.874350 | 182.666700 | 181.039750 | 185.207000 | 184.593200 | 187.572500 | 181.756450 | 182.163750 | 186.222400 | 188.067200 | 188.656400 | 186.388500 | 186.091700 | 186.865350 | 183.998100 | 180.565000 | 183.087000 | 186.870600 | 185.016900 | 185.398650 | 185.242100 | 185.500900 | 184.063700 | 178.771250 | 183.317400 | 185.837900 | 184.598550 | 189.104800 | 197.755400 | 216.909400 |
| 75% | 193.506525 | 195.456700 | 200.566625 | 196.202900 | 201.427900 | 206.590025 | 212.005200 | 228.674800 | 224.933700 | 213.084700 | 202.947950 | 199.191400 | 211.806100 | 203.739425 | 202.335100 | NaN | 180.847500 | 183.353950 | 179.997400 | 178.640525 | 180.758200 | 184.004900 | 182.318200 | 186.598750 | 185.568800 | 188.728500 | 183.492350 | 185.648100 | 188.735500 | 189.675775 | 190.943900 | 187.730400 | 186.814500 | 188.309100 | 185.115575 | 181.883600 | 185.073400 | 188.814825 | 186.673950 | 186.679875 | 186.736175 | 186.781700 | 185.488850 | 180.311300 | 184.893100 | 187.517000 | 185.883200 | 191.810400 | 199.843200 | 219.817300 |
| 97.5% | 199.657700 | 201.730500 | 208.237943 | 202.387590 | 207.193985 | 216.846275 | 216.898100 | 234.468600 | 228.393905 | 221.701650 | 208.034605 | 203.798400 | 215.098478 | 208.161978 | 206.627045 | NaN | 184.819380 | 186.430540 | 182.485900 | 180.880410 | 183.641800 | 187.273000 | 186.047500 | 190.246060 | 190.088538 | 192.933750 | 187.699800 | 201.029195 | 193.435400 | 193.506075 | 195.945378 | 191.205827 | 192.851587 | 198.140497 | 191.815540 | 185.757200 | 188.576460 | 197.989413 | 190.095413 | 189.502995 | 190.007938 | 190.133555 | 188.964790 | 183.870200 | 188.851000 | 191.083300 | 190.000837 | 206.197340 | 212.630538 | 230.677750 |
| max | 203.096500 | 205.228400 | 214.767400 | 206.963700 | 214.307800 | 236.014300 | 218.942500 | 238.814700 | 237.626800 | 250.433600 | 227.325100 | 209.575000 | 221.945800 | 220.964700 | 215.966600 | NaN | 185.390900 | 187.474800 | 184.718700 | 182.282300 | 191.674300 | 189.902700 | 189.911700 | 194.074100 | 193.213600 | 195.260800 | 189.093900 | 205.717700 | 197.244900 | 197.065200 | 198.978300 | 193.347500 | 194.561500 | 202.476900 | 193.925200 | 187.607900 | 193.462700 | 200.159900 | 192.587900 | 195.803900 | 193.579300 | 195.158900 | 196.146300 | 188.484300 | 194.026400 | 196.229200 | 195.779400 | 212.002500 | 217.640700 | 233.965000 |
OptRev_Azimuth(rt)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 192.419556 | 193.190410 | 200.780726 | 193.775473 | 201.874735 | 209.344089 | 215.746731 | 232.223856 | 217.207918 | 205.600715 | 199.597918 | 191.679516 | 194.891780 | 200.748629 | 199.115903 | NaN | 178.612782 | 182.162535 | 178.759938 | 178.205422 | 184.284183 | 187.462607 | 182.979407 | 183.126053 | 183.660206 | 184.207398 | 179.850687 | 181.361563 | 184.710518 | 186.259108 | 187.678018 | 186.874467 | 186.739800 | 188.699230 | 188.384414 | 180.646990 | 188.052197 | 191.887100 | 186.445818 | 183.659708 | 185.506105 | 185.398152 | 182.272638 | 178.481143 | 182.539454 | 185.217255 | 185.694667 | 185.061463 | 204.784867 | 222.026744 |
| std | 4.702467 | 4.439599 | 10.535536 | 5.836348 | 5.967621 | 6.092274 | 5.206692 | 3.020238 | 5.270372 | 5.810476 | 5.262522 | 3.802079 | 4.038446 | 4.001652 | 5.397037 | NaN | 1.968565 | 2.115878 | 1.778720 | 1.461869 | 2.010019 | 2.771401 | 2.269687 | 2.264569 | 2.502218 | 2.512231 | 2.620156 | 5.179104 | 5.314334 | 3.391551 | 3.281953 | 3.611718 | 3.377352 | 4.308749 | 3.338418 | 3.656080 | 3.710345 | 4.879803 | 3.849713 | 2.300042 | 2.998700 | 2.165532 | 5.165609 | 3.078277 | 2.703694 | 2.537915 | 2.458549 | 4.754723 | 10.810255 | 11.226809 |
| min | 176.891100 | 179.317900 | 179.337300 | 175.180000 | 173.988400 | 186.012900 | 195.925700 | 220.084200 | 189.388500 | 189.477300 | 176.793600 | 172.359300 | 179.342300 | 184.775900 | 161.297400 | NaN | 174.413500 | 177.281300 | 174.167200 | 173.672800 | 179.169100 | 178.258600 | 177.372300 | 176.976100 | 177.971000 | 178.385400 | 174.137500 | 170.762300 | 174.029100 | 176.094200 | 177.487100 | 178.252700 | 179.165100 | 178.329800 | 180.759800 | 168.939200 | 178.528100 | 180.572500 | 178.178300 | 176.179600 | 175.086300 | 178.244200 | 171.233100 | 170.269100 | 174.058000 | 171.410000 | 173.917300 | 169.246800 | 169.520600 | 178.114600 |
| 2.5% | 185.378520 | 185.386300 | 186.452105 | 184.839625 | 192.780600 | 199.094738 | 206.428880 | 224.933360 | 205.386980 | 197.779675 | 189.494650 | 184.738080 | 188.322770 | 193.972232 | 189.141645 | NaN | 175.009500 | 178.405750 | 175.779600 | 175.600200 | 180.679575 | 181.815670 | 178.908987 | 178.632170 | 179.419418 | 179.622350 | 175.975572 | 173.505115 | 176.126792 | 179.933300 | 182.333650 | 178.978800 | 181.102345 | 180.725900 | 182.236883 | 173.105678 | 181.596475 | 184.657025 | 180.531325 | 179.432530 | 179.738600 | 181.719095 | 177.056502 | 173.514100 | 178.279240 | 180.715850 | 181.675938 | 176.926700 | 184.417100 | 196.507450 |
| 25% | 188.727050 | 190.004400 | 194.272225 | 189.162450 | 196.856425 | 205.205075 | 211.879200 | 230.699200 | 214.035850 | 202.663800 | 196.780150 | 189.489300 | 192.369025 | 198.797950 | 195.723550 | NaN | 177.290300 | 180.742650 | 177.576800 | 177.220075 | 182.966400 | 185.604800 | 181.343925 | 181.595500 | 181.997025 | 182.542000 | 178.057100 | 177.384000 | 180.255775 | 183.937050 | 185.272200 | 184.456825 | 184.073400 | 186.175625 | 186.936675 | 178.836875 | 185.549150 | 189.614400 | 184.823775 | 182.066375 | 183.488800 | 183.960900 | 179.527600 | 176.301375 | 180.707500 | 183.570000 | 184.100950 | 181.927400 | 196.678225 | 213.277000 |
| 50% | 191.472900 | 192.429100 | 197.555100 | 192.863800 | 201.224300 | 209.182600 | 215.506400 | 232.543000 | 217.175300 | 204.661000 | 199.122000 | 191.535300 | 194.495900 | 200.571650 | 199.049100 | NaN | 178.305200 | 182.168000 | 178.593200 | 178.162800 | 184.173800 | 187.745000 | 182.808000 | 183.138300 | 183.541350 | 184.412900 | 179.225900 | 180.583150 | 184.520250 | 186.351850 | 187.288500 | 186.976950 | 187.072100 | 188.584200 | 187.972600 | 180.741450 | 187.859900 | 190.613700 | 185.667800 | 183.592000 | 185.361400 | 185.246800 | 181.248900 | 178.145300 | 182.150500 | 184.974400 | 185.399750 | 184.543600 | 204.963200 | 227.872500 |
| 75% | 196.120775 | 196.240100 | 206.011875 | 197.658350 | 206.373325 | 213.358400 | 219.259000 | 234.236400 | 221.174700 | 207.423350 | 202.482550 | 193.976700 | 197.030375 | 202.249475 | 202.869050 | NaN | 179.702900 | 183.400100 | 179.493700 | 178.993425 | 185.599025 | 189.283500 | 184.472825 | 184.377150 | 185.138250 | 185.834900 | 181.604625 | 183.998650 | 188.896375 | 188.404500 | 190.337850 | 189.445800 | 189.077150 | 190.753500 | 189.932700 | 181.915300 | 190.542900 | 193.663900 | 186.765675 | 185.144475 | 187.505575 | 186.609850 | 183.073900 | 180.237450 | 183.852100 | 186.713700 | 186.984525 | 186.999600 | 214.792300 | 230.588200 |
| 97.5% | 201.466358 | 201.567800 | 231.673400 | 205.103000 | 212.672180 | 218.906213 | 225.662500 | 237.494700 | 226.154075 | 219.152500 | 210.302595 | 199.438920 | 203.557652 | 211.948830 | 208.273320 | NaN | 183.127020 | 186.729470 | 183.166100 | 181.371900 | 188.596113 | 191.970150 | 187.485813 | 187.593350 | 189.509525 | 189.097050 | 185.631000 | 195.603872 | 196.050263 | 192.923800 | 193.911162 | 192.496375 | 192.700745 | 197.603997 | 195.171310 | 189.641100 | 194.952765 | 202.558600 | 195.474750 | 188.425652 | 191.404000 | 190.376400 | 201.984610 | 185.319500 | 189.151120 | 191.159350 | 191.445125 | 197.062740 | 218.988680 | 235.007500 |
| max | 204.880000 | 206.639100 | 240.535300 | 207.925600 | 224.193000 | 238.730800 | 229.308800 | 240.760500 | 230.219500 | 243.097700 | 227.128200 | 211.773900 | 238.986800 | 226.608700 | 216.146700 | NaN | 184.041100 | 192.014600 | 185.499500 | 182.262000 | 190.000400 | 196.522300 | 192.202100 | 189.218300 | 190.724600 | 190.830300 | 188.550600 | 201.191100 | 202.356800 | 196.271800 | 196.880100 | 196.778300 | 194.855900 | 200.426500 | 199.636100 | 190.466700 | 201.224600 | 208.844200 | 200.090500 | 192.316500 | 195.193400 | 195.685300 | 207.985300 | 204.263500 | 202.003100 | 195.185700 | 205.056800 | 205.838500 | 226.641200 | 238.272400 |
OptCF_Tilt(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 31.633125 | 31.636149 | 31.631682 | 31.619229 | 31.614647 | 31.612739 | 31.612102 | 31.610307 | 28.292342 | 28.289521 | 28.292366 | 28.296933 | 28.286637 | 28.284956 | 28.281295 | NaN | 36.638339 | 36.636416 | 36.639161 | 36.632402 | 36.741684 | 36.770468 | 36.773347 | 34.263909 | 34.263430 | 34.266644 | 34.237802 | 31.512273 | 31.519112 | 31.385628 | 31.431940 | 34.537130 | 34.511823 | 34.535154 | 34.523170 | 34.515811 | 34.512483 | 34.507014 | 34.507014 | 33.858432 | 33.850499 | 33.658997 | 33.677204 | 33.666880 | 33.677965 | 33.660617 | 33.672899 | 34.148487 | 33.906856 | 33.227356 |
| std | 1.176165 | 1.177897 | 1.176649 | 1.173946 | 1.174922 | 1.176129 | 1.176252 | 1.174554 | 1.796051 | 1.800043 | 1.801218 | 1.801404 | 1.804111 | 1.805076 | 1.807607 | NaN | 1.026728 | 1.013185 | 1.033411 | 1.022697 | 1.012513 | 1.001989 | 0.963586 | 2.013093 | 2.072248 | 2.053242 | 2.055379 | 3.520039 | 3.514637 | 3.502522 | 3.490558 | 1.779196 | 1.774714 | 1.761480 | 1.764593 | 1.764929 | 1.764260 | 1.765927 | 1.765927 | 1.482136 | 1.481705 | 1.558943 | 1.561142 | 1.553388 | 1.550470 | 1.543199 | 1.545698 | 1.310803 | 1.094678 | 1.723585 |
| min | 28.692800 | 28.692800 | 28.692800 | 28.692800 | 28.692800 | 28.692800 | 28.692800 | 28.692800 | 21.678000 | 21.678000 | 21.678000 | 21.678000 | 21.678000 | 21.678000 | 21.678000 | NaN | 34.046300 | 34.046300 | 34.046300 | 34.046300 | 34.046300 | 34.046300 | 34.046300 | 29.648300 | 29.648300 | 29.648300 | 29.648300 | 25.816400 | 25.816400 | 25.816400 | 25.816400 | 29.549900 | 29.549900 | 29.549900 | 29.549900 | 29.549900 | 29.549900 | 29.549900 | 29.549900 | 29.418000 | 29.418000 | 28.778100 | 28.778100 | 28.778100 | 28.778100 | 28.778100 | 28.778100 | 30.725900 | 30.725900 | 30.233300 |
| 2.5% | 29.253600 | 29.253600 | 29.253600 | 29.253600 | 29.247225 | 29.247475 | 29.247600 | 29.244100 | 24.784270 | 24.701335 | 24.707405 | 24.710440 | 24.711957 | 24.711957 | 24.701335 | NaN | 34.487800 | 34.487800 | 34.487800 | 34.487800 | 34.613000 | 34.625300 | 34.875800 | 31.089660 | 31.294670 | 31.373520 | 30.936645 | 26.092878 | 26.095842 | 26.119563 | 26.107702 | 30.749800 | 30.749800 | 30.749800 | 30.747573 | 30.748382 | 30.748585 | 30.748788 | 30.748788 | 31.288827 | 31.287112 | 30.895160 | 30.893000 | 30.889700 | 30.916120 | 30.891350 | 30.889700 | 32.342480 | 32.383000 | 30.847000 |
| 25% | 30.934500 | 30.943300 | 30.930150 | 30.928700 | 30.926700 | 30.926250 | 30.926100 | 30.914800 | 26.933950 | 26.931200 | 26.928000 | 26.933900 | 26.923600 | 26.898625 | 26.895400 | NaN | 35.962300 | 35.968400 | 35.968400 | 35.968400 | 36.057600 | 36.142000 | 36.200050 | 32.688850 | 32.582200 | 32.586600 | 32.582200 | 28.173500 | 28.176950 | 28.173500 | 28.173500 | 33.065025 | 33.062800 | 33.136500 | 33.093300 | 33.065025 | 33.065450 | 33.062800 | 33.062800 | 32.434900 | 32.430000 | 32.291150 | 32.297500 | 32.295150 | 32.300300 | 32.295200 | 32.299625 | 33.282400 | 33.217600 | 31.506200 |
| 50% | 31.682700 | 31.682000 | 31.679450 | 31.676800 | 31.675200 | 31.675200 | 31.675200 | 31.662200 | 28.899700 | 28.891500 | 28.899700 | 28.918100 | 28.891300 | 28.891300 | 28.891100 | NaN | 36.696600 | 36.680800 | 36.647800 | 36.643600 | 36.734650 | 36.792300 | 36.783200 | 33.958100 | 33.914700 | 33.919800 | 33.909600 | 31.950500 | 31.950500 | 31.782500 | 31.833300 | 35.402700 | 35.367000 | 35.384850 | 35.367000 | 35.367000 | 35.367000 | 35.367000 | 35.367000 | 34.214300 | 34.211700 | 33.904300 | 33.944400 | 33.941500 | 33.976600 | 33.941500 | 33.986600 | 33.838100 | 33.621300 | 33.236400 |
| 75% | 32.435850 | 32.436500 | 32.433900 | 32.409100 | 32.408300 | 32.408300 | 32.408300 | 32.408300 | 29.760100 | 29.760800 | 29.768000 | 29.769800 | 29.763575 | 29.763575 | 29.762650 | NaN | 37.319800 | 37.319800 | 37.322600 | 37.319800 | 37.377075 | 37.414600 | 37.350925 | 35.658450 | 35.743200 | 35.743200 | 35.684400 | 34.119025 | 34.128875 | 33.974800 | 34.062600 | 35.853400 | 35.833000 | 35.829925 | 35.773750 | 35.758925 | 35.749950 | 35.740975 | 35.740975 | 35.100100 | 35.096475 | 35.032900 | 35.049300 | 35.032200 | 35.039200 | 35.021600 | 35.030350 | 34.865100 | 34.379600 | 34.350000 |
| 97.5% | 33.732000 | 33.775800 | 33.775800 | 33.732000 | 33.732000 | 33.732000 | 33.732000 | 33.732000 | 30.703435 | 30.716665 | 30.720035 | 30.719880 | 30.719803 | 30.719803 | 30.720345 | NaN | 38.838930 | 38.827515 | 38.710400 | 38.682400 | 38.639650 | 38.638720 | 38.633412 | 38.634015 | 38.675297 | 38.666910 | 38.661878 | 38.240617 | 38.223622 | 38.087663 | 37.905757 | 36.331700 | 36.331700 | 36.331700 | 36.331700 | 36.331700 | 36.331700 | 36.331700 | 36.331700 | 36.061900 | 36.061900 | 36.048570 | 36.017100 | 35.999837 | 35.999720 | 35.986100 | 35.991000 | 37.138500 | 36.902842 | 36.873000 |
| max | 35.208800 | 35.208800 | 35.208800 | 35.208800 | 35.208800 | 35.208800 | 35.208800 | 35.208800 | 31.572000 | 31.572000 | 31.572000 | 31.572000 | 31.572000 | 31.572000 | 31.572000 | NaN | 39.647300 | 39.647300 | 39.647300 | 39.647300 | 39.647300 | 39.647300 | 39.647300 | 39.624200 | 39.624200 | 39.624200 | 39.624200 | 39.624200 | 39.624200 | 39.624200 | 39.624200 | 36.604600 | 36.604600 | 36.604600 | 36.604600 | 36.604600 | 36.604600 | 36.604600 | 36.604600 | 36.820200 | 36.820200 | 36.820200 | 36.820200 | 36.820200 | 36.820200 | 36.820200 | 36.820200 | 38.365400 | 38.365400 | 38.365400 |
OptRev_Tilt(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 32.434073 | 30.633941 | 32.835953 | 30.765654 | 31.016143 | 30.673733 | 33.017629 | 38.497415 | 27.158319 | 27.499473 | 26.510159 | 27.108373 | 26.559197 | 27.498930 | 26.800499 | NaN | 36.095719 | 36.766789 | 41.460856 | 44.048760 | 41.896108 | 37.856136 | 40.551380 | 31.803550 | 31.325470 | 31.795204 | 32.799691 | 30.733129 | 29.889822 | 30.381382 | 30.052923 | 33.230544 | 32.748598 | 33.439968 | 35.475702 | 39.672397 | 36.639324 | 33.802370 | 35.413205 | 31.460275 | 30.223222 | 31.803913 | 31.404158 | 35.811761 | 32.999382 | 32.403869 | 33.423539 | 32.095429 | 33.940439 | 36.982576 |
| std | 1.191404 | 1.370207 | 1.345048 | 1.343109 | 1.537241 | 1.555587 | 1.718495 | 2.353140 | 1.633360 | 1.658016 | 1.734769 | 2.105055 | 1.591631 | 1.960881 | 1.584643 | NaN | 1.142663 | 1.249320 | 1.413296 | 1.147406 | 1.075686 | 1.176071 | 1.184998 | 2.556460 | 2.426705 | 2.012710 | 2.423249 | 4.756844 | 3.972121 | 3.445544 | 3.818540 | 2.032493 | 1.743350 | 2.062154 | 2.712845 | 3.105415 | 2.663766 | 2.725364 | 2.335739 | 1.400214 | 1.372527 | 1.386078 | 1.503773 | 2.629449 | 2.185292 | 1.382845 | 1.696028 | 1.314602 | 1.215821 | 3.092808 |
| min | 29.341200 | 27.594800 | 29.575500 | 27.449100 | 25.430300 | 26.079300 | 28.415900 | 33.719100 | 20.065000 | 21.519300 | 20.845500 | 13.562100 | 22.031500 | 19.448500 | 21.152200 | NaN | 33.052900 | 33.693800 | 38.209100 | 41.187100 | 39.217100 | 34.656000 | 35.821200 | 27.084500 | 26.168800 | 27.757500 | 27.290400 | 20.604400 | 23.424900 | 24.164800 | 23.523100 | 27.496300 | 27.894000 | 28.045800 | 28.921200 | 30.589300 | 30.328200 | 26.745600 | 28.045500 | 27.375500 | 26.092500 | 27.923600 | 27.236000 | 28.861800 | 28.602800 | 28.281800 | 27.898000 | 29.292700 | 30.277800 | 30.378200 |
| 2.5% | 30.093837 | 28.346300 | 30.395188 | 28.162050 | 27.038660 | 26.903613 | 29.168180 | 34.734680 | 23.792285 | 24.175300 | 22.584300 | 22.701340 | 23.058705 | 23.102500 | 23.555700 | NaN | 33.900930 | 34.321300 | 38.741100 | 41.808170 | 39.860700 | 35.092500 | 37.707800 | 28.407700 | 28.085693 | 29.228960 | 29.427295 | 21.732817 | 23.679503 | 25.002775 | 24.264900 | 29.516320 | 28.955715 | 29.453300 | 30.282400 | 32.735140 | 31.191400 | 27.824187 | 29.316525 | 29.123000 | 27.775487 | 29.467600 | 28.902435 | 31.879787 | 29.685500 | 29.976150 | 30.678850 | 30.174900 | 32.152455 | 32.779900 |
| 25% | 31.613900 | 29.604200 | 31.901800 | 30.006300 | 30.361775 | 29.813600 | 31.974400 | 36.984000 | 26.145300 | 26.426750 | 25.373750 | 26.085500 | 25.437975 | 26.284900 | 25.727850 | NaN | 35.459400 | 35.867400 | 40.514400 | 43.275275 | 41.149550 | 37.080400 | 39.894500 | 30.006150 | 29.550475 | 30.243400 | 31.130000 | 26.811800 | 26.424800 | 27.622925 | 26.629900 | 31.495725 | 31.771225 | 31.562750 | 33.078125 | 37.530325 | 34.016750 | 31.240725 | 33.872150 | 30.197125 | 29.029700 | 30.603200 | 30.101800 | 33.632225 | 30.939600 | 31.178800 | 31.987100 | 31.157400 | 33.068900 | 34.325500 |
| 50% | 32.503950 | 30.606300 | 32.829900 | 30.847600 | 31.285200 | 30.758350 | 33.263900 | 38.299300 | 27.264700 | 27.899400 | 26.770600 | 27.694800 | 27.041500 | 28.082200 | 27.243900 | NaN | 35.974800 | 36.864600 | 41.501600 | 44.057000 | 41.997200 | 38.025300 | 40.582200 | 30.898600 | 30.652350 | 31.161400 | 32.067950 | 31.824600 | 30.712500 | 30.714500 | 30.402800 | 34.189900 | 33.765650 | 34.379700 | 36.667450 | 41.175600 | 37.852900 | 35.087100 | 36.214950 | 31.584450 | 30.474600 | 31.928400 | 31.653600 | 35.654200 | 33.289600 | 32.631900 | 33.369900 | 31.766000 | 33.713200 | 36.739100 |
| 75% | 33.324400 | 31.588900 | 33.752850 | 31.626300 | 32.006975 | 31.690625 | 34.333700 | 39.873100 | 28.510250 | 28.723800 | 27.950350 | 28.525700 | 27.789200 | 28.988350 | 27.983850 | NaN | 36.688600 | 37.723250 | 42.192200 | 44.824125 | 42.686100 | 38.640400 | 41.354600 | 33.833500 | 32.621300 | 32.944000 | 34.514750 | 34.671100 | 32.811725 | 32.781550 | 32.793450 | 34.882100 | 34.008425 | 35.240675 | 37.800700 | 41.859700 | 38.420800 | 36.044200 | 36.886550 | 32.583600 | 31.273825 | 32.867900 | 32.506325 | 37.498500 | 34.438300 | 33.522200 | 34.652325 | 32.836700 | 34.512200 | 38.585500 |
| 97.5% | 34.697930 | 33.749200 | 35.358770 | 33.179060 | 33.334300 | 33.383212 | 35.664300 | 43.654320 | 29.589710 | 29.853600 | 29.037400 | 30.042540 | 28.760005 | 29.986728 | 29.120540 | NaN | 38.875460 | 39.273955 | 44.616600 | 46.418548 | 43.681712 | 39.901600 | 42.665363 | 37.415685 | 36.849260 | 36.468360 | 38.068022 | 38.391570 | 36.795325 | 37.541300 | 37.025157 | 35.555665 | 35.075135 | 35.718350 | 39.254500 | 42.971477 | 40.297300 | 36.673800 | 38.044787 | 33.946085 | 32.570137 | 34.441500 | 34.360100 | 41.356437 | 37.040400 | 34.705050 | 36.600800 | 34.891200 | 36.878600 | 43.725600 |
| max | 35.426100 | 37.506100 | 37.698200 | 35.062000 | 34.704800 | 35.397700 | 36.711600 | 53.609600 | 34.564400 | 33.454600 | 29.735300 | 32.713200 | 30.904400 | 32.802500 | 34.511100 | NaN | 39.716500 | 40.364000 | 45.267600 | 47.144900 | 44.151100 | 40.659100 | 44.410700 | 38.589300 | 37.990500 | 37.576600 | 40.078100 | 40.340200 | 39.233500 | 39.071600 | 38.800800 | 36.693000 | 35.370800 | 37.234400 | 39.818800 | 43.293700 | 40.701200 | 37.206900 | 38.787600 | 35.555800 | 33.704400 | 35.304700 | 36.418400 | 43.602900 | 39.317600 | 37.706700 | 38.356800 | 36.087000 | 38.077800 | 45.045700 |
OptRev_Tilt(rt)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 33.013682 | 32.280974 | 32.587352 | 31.747961 | 31.375414 | 32.692273 | 37.283220 | 43.217181 | 26.120193 | 28.354953 | 27.503510 | 27.585834 | 25.907481 | 27.613885 | 26.707719 | NaN | 35.794053 | 35.997743 | 40.585449 | 44.226671 | 41.592995 | 37.554075 | 41.550309 | 31.509001 | 31.378751 | 31.751390 | 32.689352 | 30.458299 | 29.766970 | 30.186035 | 30.041975 | 32.532563 | 32.495469 | 32.813139 | 34.815097 | 40.012609 | 36.587045 | 34.450624 | 36.392932 | 30.878538 | 29.534914 | 31.445787 | 31.204277 | 35.299700 | 32.658465 | 32.243852 | 33.553628 | 31.959440 | 36.893790 | 42.079869 |
| std | 1.447432 | 1.361375 | 2.100029 | 1.422111 | 1.669575 | 2.480109 | 1.753088 | 2.082641 | 1.584116 | 2.242693 | 1.882021 | 1.962514 | 1.917089 | 2.143363 | 1.873789 | NaN | 1.186228 | 1.288861 | 1.437732 | 1.041059 | 1.099977 | 1.255332 | 1.227493 | 2.312640 | 2.210704 | 2.005046 | 2.518508 | 4.407745 | 4.114228 | 3.473793 | 3.812324 | 1.925641 | 1.739007 | 1.898587 | 2.336692 | 3.286824 | 2.899078 | 2.829387 | 2.504529 | 1.190845 | 1.226819 | 1.436687 | 1.534987 | 2.787362 | 2.285731 | 1.468202 | 2.016650 | 1.281428 | 2.095167 | 3.401052 |
| min | 30.106700 | 29.164100 | 27.794800 | 28.461500 | 24.964700 | 26.201300 | 32.503700 | 36.953700 | 17.561400 | 14.827000 | 21.503700 | 21.883300 | 19.993300 | 20.314600 | 19.848100 | NaN | 33.008900 | 33.027600 | 37.319900 | 41.112500 | 38.802800 | 34.001600 | 36.199500 | 25.953200 | 26.233700 | 27.360200 | 26.892100 | 19.894100 | 22.827200 | 23.456600 | 23.818100 | 26.915400 | 27.782700 | 27.521800 | 29.361900 | 30.733300 | 29.283300 | 27.413500 | 28.016900 | 26.887900 | 25.967200 | 27.681100 | 25.958700 | 27.906600 | 27.303100 | 28.059600 | 28.139400 | 28.010600 | 26.288300 | 25.956600 |
| 2.5% | 30.804460 | 29.872500 | 29.651142 | 29.107265 | 26.725038 | 27.612838 | 33.503300 | 39.646380 | 22.932245 | 23.961500 | 23.622800 | 22.957500 | 21.856578 | 22.717668 | 22.503700 | NaN | 33.559130 | 33.559820 | 37.741800 | 42.052485 | 39.295412 | 34.551730 | 38.969000 | 28.077715 | 28.425050 | 28.986500 | 29.311735 | 21.428640 | 23.275745 | 24.597763 | 24.327258 | 29.105970 | 28.766383 | 28.843300 | 30.525600 | 33.093800 | 29.695445 | 28.155975 | 29.746625 | 28.851917 | 27.331400 | 29.074585 | 28.529245 | 30.746200 | 28.969940 | 29.749800 | 30.422663 | 29.794880 | 32.950192 | 35.415150 |
| 25% | 32.082500 | 31.420000 | 31.282800 | 30.624050 | 30.578500 | 31.062600 | 36.095700 | 42.116600 | 24.994850 | 27.085800 | 26.132150 | 26.561800 | 24.369325 | 26.285100 | 25.507200 | NaN | 35.025700 | 35.033700 | 39.707400 | 43.558750 | 40.879075 | 36.720800 | 40.858400 | 29.861500 | 29.769575 | 30.243300 | 30.995875 | 27.004900 | 25.760600 | 27.183450 | 26.429600 | 31.214075 | 31.483975 | 31.275050 | 33.169650 | 37.715600 | 33.867500 | 32.783000 | 34.971575 | 29.922875 | 28.501675 | 30.281050 | 30.029200 | 32.967500 | 30.620100 | 30.944900 | 31.907000 | 31.221700 | 35.372375 | 39.436400 |
| 50% | 32.996450 | 32.153300 | 32.235900 | 31.826800 | 31.598300 | 32.560900 | 37.408700 | 42.938400 | 26.359300 | 28.916500 | 27.770400 | 28.069400 | 26.571100 | 28.254350 | 27.173300 | NaN | 35.660800 | 36.064800 | 40.559600 | 44.302500 | 41.679900 | 37.745200 | 41.544700 | 30.889100 | 30.894400 | 30.993300 | 31.694300 | 31.372800 | 30.660250 | 30.584800 | 30.425200 | 33.383100 | 33.399950 | 33.386500 | 35.394400 | 41.479100 | 37.929100 | 35.411700 | 37.232400 | 30.964150 | 29.603600 | 31.433700 | 31.309800 | 35.124700 | 32.899300 | 32.404400 | 33.300000 | 31.831800 | 36.793300 | 43.122500 |
| 75% | 33.845525 | 33.074200 | 33.437225 | 32.785800 | 32.487800 | 34.734200 | 38.531500 | 44.184700 | 27.406850 | 29.664450 | 29.094550 | 28.883700 | 27.365450 | 29.147500 | 28.004000 | NaN | 36.541700 | 37.045150 | 41.181400 | 44.905800 | 42.415700 | 38.512100 | 42.289200 | 33.099900 | 32.563350 | 33.177500 | 34.531175 | 33.630000 | 32.812600 | 32.730550 | 32.830975 | 34.046825 | 33.661425 | 34.274250 | 36.838050 | 42.436800 | 38.776750 | 36.615925 | 38.208425 | 31.710175 | 30.548300 | 32.453200 | 32.230275 | 37.436450 | 34.135700 | 33.395600 | 34.949650 | 32.568200 | 38.722150 | 45.025400 |
| 97.5% | 35.317377 | 35.402700 | 38.904422 | 34.430595 | 33.989800 | 36.677412 | 40.418200 | 47.713800 | 28.564540 | 32.014145 | 30.450630 | 30.605420 | 28.451600 | 30.280983 | 29.528200 | NaN | 38.597700 | 38.557300 | 43.828500 | 46.041900 | 43.448125 | 39.603460 | 43.914700 | 36.709955 | 36.417857 | 36.177880 | 38.666500 | 38.032700 | 37.033180 | 37.556213 | 37.164407 | 34.982897 | 34.639850 | 35.328800 | 38.395400 | 43.713310 | 39.792735 | 37.789600 | 39.213563 | 33.001500 | 31.583100 | 34.356045 | 34.390400 | 40.559425 | 36.973240 | 34.834300 | 37.556700 | 35.033660 | 39.795100 | 47.219650 |
| max | 52.632000 | 38.618900 | 41.422900 | 35.911900 | 35.323100 | 39.079600 | 42.940300 | 57.927100 | 29.948900 | 39.014900 | 31.493000 | 35.047000 | 35.322100 | 34.489900 | 41.200600 | NaN | 39.372500 | 39.382500 | 44.773100 | 46.874400 | 44.323600 | 40.154400 | 45.457600 | 37.511100 | 37.345300 | 36.747400 | 40.684300 | 39.920500 | 39.203900 | 39.029200 | 39.804000 | 35.190800 | 35.251100 | 35.496000 | 38.875300 | 44.680600 | 40.700400 | 38.527700 | 41.145600 | 36.261100 | 32.708100 | 35.855200 | 37.102600 | 43.767500 | 41.157400 | 38.849200 | 39.883400 | 37.128000 | 41.709400 | 48.794700 |
OptRev_Tilt(da)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 32.433031 | 30.631897 | 32.819397 | 30.766198 | 31.015674 | 30.680011 | 32.991855 | 38.182560 | 27.153895 | 27.496116 | 26.509645 | 27.105094 | 26.558860 | 27.493676 | 26.807097 | NaN | 36.095717 | 36.766594 | 41.460805 | 44.048905 | 41.896432 | 37.856348 | 40.553512 | 31.801593 | 31.326927 | 31.785395 | 32.839315 | 30.731251 | 29.882947 | 30.378875 | 30.051633 | 33.230512 | 32.748598 | 33.439831 | 35.477095 | 39.672397 | 36.640125 | 33.802405 | 35.413204 | 31.461681 | 30.222023 | 31.802937 | 31.404340 | 35.812696 | 33.000338 | 32.404383 | 33.423327 | 32.095449 | 33.936152 | 36.809625 |
| std | 1.190383 | 1.365908 | 1.327217 | 1.341794 | 1.537130 | 1.552760 | 1.720233 | 2.079906 | 1.629645 | 1.657036 | 1.734656 | 2.106024 | 1.591909 | 1.969213 | 1.579673 | NaN | 1.142661 | 1.249490 | 1.413307 | 1.147314 | 1.075551 | 1.175765 | 1.171746 | 2.551486 | 2.425534 | 1.992850 | 2.364283 | 4.753803 | 3.972376 | 3.441364 | 3.815527 | 2.032460 | 1.743350 | 2.062003 | 2.712579 | 3.105415 | 2.661978 | 2.725342 | 2.335743 | 1.399396 | 1.369956 | 1.384809 | 1.503719 | 2.629442 | 2.183649 | 1.382497 | 1.695866 | 1.314577 | 1.218868 | 3.100074 |
| min | 29.340800 | 27.592800 | 29.629200 | 27.449100 | 25.430500 | 26.079300 | 28.481500 | 33.660700 | 20.065000 | 21.542500 | 20.845500 | 13.562100 | 22.030900 | 19.448500 | 21.152200 | NaN | 33.052900 | 33.693800 | 38.209100 | 41.187100 | 39.217000 | 34.656000 | 37.164200 | 27.084500 | 26.168800 | 27.757500 | 28.284400 | 20.604400 | 23.424900 | 24.164800 | 23.523100 | 27.496300 | 27.894000 | 28.045800 | 28.921200 | 30.589300 | 30.328200 | 26.745600 | 28.045500 | 27.375300 | 26.092500 | 27.924700 | 27.236000 | 28.861800 | 28.602800 | 28.281800 | 27.891000 | 29.295100 | 30.260600 | 30.339600 |
| 2.5% | 30.093637 | 28.344400 | 30.395188 | 28.162050 | 27.038660 | 26.903613 | 29.218360 | 34.612200 | 23.792285 | 24.167290 | 22.584300 | 22.700220 | 23.058705 | 23.063207 | 23.555700 | NaN | 33.900930 | 34.321300 | 38.741100 | 41.808170 | 39.860600 | 35.092500 | 37.707800 | 28.407700 | 28.085693 | 29.228960 | 29.619187 | 21.731580 | 23.679503 | 25.002775 | 24.264900 | 29.516320 | 28.955715 | 29.453300 | 30.282400 | 32.735140 | 31.191400 | 27.824187 | 29.316525 | 29.123000 | 27.775487 | 29.467600 | 28.902435 | 31.879787 | 29.685500 | 29.978850 | 30.678850 | 30.174900 | 32.151895 | 32.698600 |
| 25% | 31.613400 | 29.602000 | 31.901800 | 30.006300 | 30.360575 | 29.813075 | 31.917000 | 36.800200 | 26.145300 | 26.426600 | 25.373750 | 26.079700 | 25.435400 | 26.253175 | 25.735400 | NaN | 35.459400 | 35.867400 | 40.514400 | 43.275275 | 41.149525 | 37.080400 | 39.894500 | 30.006150 | 29.550475 | 30.245500 | 31.130075 | 26.811800 | 26.011575 | 27.622925 | 26.625125 | 31.495725 | 31.771225 | 31.562750 | 33.078125 | 37.530325 | 34.016750 | 31.240725 | 33.872150 | 30.199150 | 29.029700 | 30.603850 | 30.103900 | 33.635875 | 30.939600 | 31.178800 | 31.987100 | 31.157400 | 33.053100 | 34.146600 |
| 50% | 32.503750 | 30.605100 | 32.829500 | 30.854500 | 31.282450 | 30.757400 | 33.253900 | 38.111600 | 27.259100 | 27.898500 | 26.777200 | 27.697500 | 27.041500 | 28.087750 | 27.270300 | NaN | 35.974800 | 36.864600 | 41.501600 | 44.057000 | 41.997000 | 38.025300 | 40.582200 | 30.898600 | 30.651950 | 31.161400 | 32.067950 | 31.821450 | 30.709850 | 30.723550 | 30.402800 | 34.189900 | 33.765650 | 34.379700 | 36.667450 | 41.175600 | 37.852900 | 35.087100 | 36.214950 | 31.589900 | 30.474600 | 31.928400 | 31.655500 | 35.654200 | 33.293400 | 32.633900 | 33.370500 | 31.766000 | 33.701300 | 36.547200 |
| 75% | 33.324225 | 31.605900 | 33.717950 | 31.626300 | 32.006950 | 31.690625 | 34.314300 | 39.579600 | 28.508150 | 28.723800 | 27.950350 | 28.525700 | 27.789025 | 28.984500 | 27.983850 | NaN | 36.688600 | 37.723250 | 42.192200 | 44.824125 | 42.686100 | 38.640400 | 41.354600 | 33.833500 | 32.621300 | 32.944000 | 34.502825 | 34.671100 | 32.812700 | 32.770175 | 32.795175 | 34.882100 | 34.008425 | 35.240675 | 37.800700 | 41.859700 | 38.420800 | 36.044200 | 36.886550 | 32.581650 | 31.273825 | 32.867500 | 32.506325 | 37.498500 | 34.438300 | 33.522000 | 34.651500 | 32.836700 | 34.512200 | 38.337900 |
| 97.5% | 34.697052 | 33.708900 | 35.343955 | 33.179060 | 33.330000 | 33.400500 | 35.664300 | 42.293100 | 29.589710 | 29.853560 | 29.037400 | 30.012900 | 28.760005 | 29.986728 | 29.119240 | NaN | 38.875460 | 39.273955 | 44.616600 | 46.418548 | 43.681637 | 39.901600 | 42.665363 | 37.415685 | 36.849260 | 36.468360 | 38.080563 | 38.389755 | 36.796797 | 37.423275 | 37.036345 | 35.555665 | 35.075135 | 35.715800 | 39.254500 | 42.971477 | 40.297300 | 36.673800 | 38.044787 | 33.946085 | 32.545500 | 34.441500 | 34.360100 | 41.356437 | 37.040400 | 34.689250 | 36.600800 | 34.891200 | 36.878600 | 43.571250 |
| max | 35.424400 | 37.329800 | 37.548200 | 35.061000 | 34.704800 | 34.965200 | 36.711600 | 46.326700 | 33.128000 | 33.485800 | 29.735300 | 32.724300 | 30.907500 | 32.802500 | 33.899200 | NaN | 39.716500 | 40.364000 | 45.267600 | 47.144900 | 44.151100 | 40.659100 | 44.216700 | 38.583500 | 37.990400 | 37.576600 | 40.078100 | 40.340200 | 39.233500 | 39.067900 | 38.823700 | 36.693000 | 35.370800 | 37.234400 | 39.818800 | 43.293700 | 40.701200 | 37.206900 | 38.787600 | 35.555800 | 33.704400 | 35.304700 | 36.418400 | 43.602900 | 39.317600 | 37.613600 | 38.356800 | 36.087000 | 38.077800 | 44.939500 |
OptRev_Tilt(rt)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2203.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.00000 | 1570.000000 | 1570.000000 | 1563.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 32.862700 | 31.960747 | 32.392670 | 31.690125 | 31.172152 | 31.766336 | 36.439825 | 41.462049 | 26.099639 | 28.348577 | 27.505172 | 27.57895 | 25.913518 | 27.643429 | 26.733333 | NaN | 35.791992 | 35.995246 | 40.585806 | 44.246919 | 41.600065 | 37.547775 | 41.469155 | 31.555894 | 31.412097 | 31.737759 | 32.773729 | 30.436923 | 29.753192 | 30.199193 | 30.033287 | 32.551441 | 32.457167 | 32.818334 | 34.833949 | 40.008777 | 36.576465 | 34.237206 | 36.268685 | 30.903334 | 29.530794 | 31.446388 | 31.203570 | 35.319856 | 32.650817 | 32.247545 | 33.540068 | 32.307866 | 36.100192 | 38.710147 |
| std | 1.250700 | 1.304190 | 1.847937 | 1.383531 | 1.602483 | 1.811785 | 1.552193 | 1.433022 | 1.574081 | 2.226812 | 1.885069 | 1.96881 | 1.918536 | 2.121665 | 1.834009 | NaN | 1.185922 | 1.290526 | 1.437980 | 1.045128 | 1.096856 | 1.252827 | 1.142840 | 2.263263 | 2.246465 | 1.923680 | 2.452420 | 4.389655 | 4.096931 | 3.455796 | 3.789929 | 1.934298 | 1.740755 | 1.882429 | 2.321702 | 3.287919 | 2.871999 | 2.753653 | 2.457807 | 1.184946 | 1.218671 | 1.435750 | 1.533717 | 2.782969 | 2.286188 | 1.466995 | 1.993674 | 1.270149 | 1.805027 | 1.883778 |
| min | 29.915200 | 28.911500 | 28.717700 | 28.442900 | 25.063300 | 26.291200 | 31.896100 | 36.362300 | 16.872900 | 15.102900 | 21.505000 | 22.00310 | 20.373900 | 20.207700 | 19.755100 | NaN | 33.009800 | 33.027600 | 37.319900 | 41.142000 | 38.810400 | 34.027300 | 38.382600 | 26.898900 | 26.255700 | 27.360000 | 28.244800 | 19.938400 | 22.947800 | 23.456400 | 23.857700 | 26.883300 | 27.761200 | 27.513300 | 29.355100 | 30.733600 | 29.280300 | 27.417100 | 27.997900 | 27.415600 | 25.968500 | 27.679800 | 25.958700 | 27.906300 | 27.386700 | 28.059200 | 28.139400 | 28.695200 | 30.375100 | 28.617500 |
| 2.5% | 30.618153 | 29.593400 | 29.710100 | 29.089950 | 26.752715 | 27.730337 | 33.059340 | 38.203560 | 22.970145 | 23.952410 | 23.578750 | 22.84280 | 21.856478 | 22.744630 | 22.508300 | NaN | 33.571700 | 33.559820 | 37.741800 | 42.062385 | 39.288800 | 34.563000 | 38.973200 | 28.466085 | 28.402815 | 29.090120 | 29.842225 | 21.436183 | 23.289538 | 24.686737 | 24.448100 | 29.116850 | 28.734390 | 28.862200 | 30.550200 | 33.082600 | 29.821475 | 28.181500 | 29.720750 | 28.905500 | 27.335675 | 29.070590 | 28.529245 | 30.820187 | 28.971560 | 29.825950 | 30.451825 | 30.386220 | 32.574055 | 34.743000 |
| 25% | 31.969150 | 31.103100 | 31.228400 | 30.723400 | 30.467375 | 30.629600 | 35.391500 | 40.743900 | 24.989950 | 27.051200 | 26.133050 | 26.55460 | 24.386625 | 26.289975 | 25.540650 | NaN | 35.025400 | 35.022400 | 39.707400 | 43.573075 | 40.862775 | 36.698800 | 40.833750 | 29.913250 | 29.769925 | 30.329500 | 31.116275 | 26.953975 | 25.725300 | 27.220500 | 26.456000 | 31.237250 | 31.471200 | 31.289700 | 33.160475 | 37.713700 | 33.836800 | 32.657725 | 34.850225 | 29.944375 | 28.502225 | 30.279700 | 30.029600 | 32.995700 | 30.621000 | 30.944200 | 31.906900 | 31.464100 | 34.697200 | 37.843300 |
| 50% | 32.864500 | 31.871500 | 32.144500 | 31.692100 | 31.404500 | 31.822400 | 36.650500 | 41.635800 | 26.309600 | 28.927600 | 27.772300 | 28.06870 | 26.567400 | 28.296400 | 27.223300 | NaN | 35.618200 | 36.064800 | 40.559600 | 44.327900 | 41.673700 | 37.741000 | 41.499100 | 30.942300 | 30.907850 | 31.019400 | 31.963200 | 31.371050 | 30.664500 | 30.757700 | 30.453200 | 33.374850 | 33.387600 | 33.432750 | 35.564900 | 41.485050 | 37.923700 | 34.793150 | 37.116850 | 30.989850 | 29.594150 | 31.447100 | 31.314250 | 35.150100 | 32.886000 | 32.420800 | 33.290850 | 32.040100 | 35.914800 | 38.732200 |
| 75% | 33.704500 | 32.796400 | 33.297100 | 32.663050 | 32.215225 | 33.052100 | 37.484200 | 42.374400 | 27.373300 | 29.653200 | 29.093250 | 28.86750 | 27.365350 | 29.168375 | 28.042250 | NaN | 36.541700 | 37.046500 | 41.181400 | 44.929500 | 42.428050 | 38.531600 | 42.165200 | 33.050900 | 32.713150 | 33.113000 | 34.606625 | 33.580950 | 32.884750 | 32.738025 | 32.819100 | 34.066625 | 33.616050 | 34.279700 | 36.791800 | 42.433325 | 38.735700 | 36.542700 | 38.160100 | 31.745050 | 30.553800 | 32.458400 | 32.234250 | 37.434650 | 34.113900 | 33.406300 | 34.923900 | 32.966300 | 37.827750 | 39.902600 |
| 97.5% | 35.128905 | 34.948100 | 38.055503 | 34.275715 | 33.615600 | 34.890000 | 39.129800 | 43.948660 | 28.543425 | 32.000005 | 30.500315 | 30.59834 | 28.456400 | 30.288245 | 29.524600 | NaN | 38.597700 | 38.557200 | 43.832500 | 46.065195 | 43.471500 | 39.652200 | 43.615350 | 36.671900 | 36.599592 | 36.157110 | 38.624203 | 37.992842 | 37.003872 | 37.120050 | 37.125565 | 35.059595 | 34.567905 | 35.320100 | 38.376200 | 43.697928 | 39.770275 | 37.328025 | 38.925700 | 33.017900 | 31.540588 | 34.370385 | 34.379200 | 40.598562 | 36.923740 | 34.818500 | 37.433200 | 35.249280 | 38.765000 | 42.429000 |
| max | 42.595700 | 36.629200 | 40.893700 | 35.661200 | 34.667100 | 36.427900 | 41.413900 | 45.690200 | 29.940900 | 39.014500 | 31.521800 | 35.04380 | 35.281600 | 34.358700 | 36.360100 | NaN | 39.372500 | 39.382200 | 44.776900 | 46.907700 | 44.341200 | 40.162000 | 44.720300 | 37.499400 | 37.480500 | 36.636600 | 40.537000 | 39.611100 | 39.216800 | 38.838500 | 38.916200 | 35.236600 | 35.113500 | 35.485700 | 38.853300 | 44.658600 | 40.663900 | 38.281600 | 39.908500 | 36.327700 | 32.544200 | 35.855200 | 37.102600 | 43.676600 | 41.121500 | 38.366900 | 39.743900 | 37.067900 | 40.755500 | 44.551500 |
########## Baseline = mustrun
### Data-indexed parameters
data = [
'CF_OptRev/OptCF_hist,da,f,mustrun',
'CF_OptRev/OptCF_hist,rt,f,mustrun',
'CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun',
'CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun',
'Rev_OptRev/OptCF_hist,da,f,mustrun',
'Rev_OptRev/OptCF_hist,rt,f,mustrun',
'Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun',
'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun',
]
colindex = [0, 0, 1, 1, 2, 2, 3, 3,]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right',
'left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt'],
mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [slice(None),slice(None),slice(None),slice(None),
slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))
### Column-indexed parameters
ylim = [
[0.63, 1.02],
[0.63, 1.02],
[0.98,1.37],
[0.98,1.37],
]
xlim = [
[2009.4, 2018],
[2009.4, 2018],
[2009.4, 2018],
[2009.4, 2018],
]
majlocs = [0.1, 0.1, 0.1, 0.1]
minlocs = [2, 2, 2, 2,]
ylabel = [
'Capacity Factor',
'Capacity Factor',
'Revenue',
'Revenue',
]
note = [
'(must-run)',
'(curtailable)',
'(must-run)',
'(curtailable)',
]
y1 = 1.2 # 1.2 if using note, 1 if no note
y2 = 1.07 # 1.07 if using note, 1.04 if no note
gridspec_kw = {'width_ratios': [2, 2, 2, 2,]}#, 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=False, gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]],
density=True, bootstrap=bootstrap,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
format_axes=False,
)
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
ax[(row,col)].set_xlim(*xlim[col])
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(-1,0)].legend(
handles=patches, loc='lower left', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, Revenue-opt. vs. CF-opt., must-run baseline', weight='bold', y=y2, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
print(datum)
display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
| CF_OptRev/OptCF_hist,da,f,mustrun | CF_OptRev/OptCF_hist,rt,f,mustrun | CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun | CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun | Rev_OptRev/OptCF_hist,da,f,mustrun | Rev_OptRev/OptCF_hist,rt,f,mustrun | Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun | Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun | |
|---|---|---|---|---|---|---|---|---|
| count | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 | 2209.000000 |
| mean | 0.935713 | 0.896420 | 0.890050 | 0.781320 | 1.042364 | 1.130232 | 1.053642 | 1.224193 |
| std | 0.017484 | 0.023111 | 0.028419 | 0.028541 | 0.018882 | 0.039487 | 0.033171 | 0.101965 |
| min | 0.821078 | 0.774044 | 0.696302 | 0.640909 | 0.993515 | 1.032940 | 0.998301 | 1.105384 |
| 2.5% | 0.900771 | 0.856013 | 0.815125 | 0.715304 | 1.005221 | 1.058584 | 1.010603 | 1.129142 |
| 25% | 0.926839 | 0.883491 | 0.885285 | 0.765099 | 1.033260 | 1.110689 | 1.041737 | 1.168903 |
| 50% | 0.934574 | 0.895030 | 0.895518 | 0.788514 | 1.043464 | 1.128227 | 1.051820 | 1.196672 |
| 75% | 0.942720 | 0.910913 | 0.904803 | 0.803383 | 1.052679 | 1.146930 | 1.060932 | 1.244334 |
| 97.5% | 0.974518 | 0.945262 | 0.930582 | 0.817736 | 1.077262 | 1.202694 | 1.110678 | 1.420512 |
| max | 0.987178 | 0.966279 | 0.941928 | 0.841168 | 1.222468 | 1.414609 | 1.559459 | 2.159810 |
median
| CF_OptRev/OptCF_hist,da,f,mustrun | CF_OptRev/OptCF_hist,rt,f,mustrun | CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun | CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun | Rev_OptRev/OptCF_hist,da,f,mustrun | Rev_OptRev/OptCF_hist,rt,f,mustrun | Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun | Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun | |||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2010 | 0.998748 | NaN | 0.999933 | 0.999281 | 0.997796 | 0.998742 | NaN | 0.997344 | NaN | NaN | 0.999289 | 0.997175 | 0.999025 | NaN | 0.998659 | NaN | 0.999920 | 0.999082 | 0.997796 | 0.998638 | NaN | 0.991157 | NaN | NaN | 0.994048 | 0.995121 | 0.997210 | NaN | 1.001266 | NaN | 1.001588 | 1.002546 | 1.001635 | 1.003360 | NaN | 1.002519 | NaN | NaN | 1.002033 | 1.002441 | 1.003203 | NaN | 1.001283 | NaN | 1.001588 | 1.002617 | 1.001635 | 1.003387 | NaN | 1.005564 | NaN | NaN | 1.003906 | 1.007934 | 1.004289 | NaN |
| 2011 | 0.998575 | 0.965722 | 1.000060 | 0.998788 | 0.997443 | 0.998193 | NaN | 0.997797 | 0.974514 | 0.999944 | 0.999063 | 0.997963 | 0.997861 | NaN | 0.997985 | 0.964373 | 1.000053 | 0.998698 | 0.997443 | 0.998148 | NaN | 0.954237 | 0.971661 | 0.999117 | 0.993504 | 0.995763 | 0.994846 | NaN | 1.001628 | 1.051259 | 1.000664 | 1.002493 | 1.001098 | 1.003984 | NaN | 1.002440 | 1.034713 | 1.000401 | 1.002398 | 1.001928 | 1.005683 | NaN | 1.001747 | 1.051538 | 1.000665 | 1.002526 | 1.001098 | 1.004008 | NaN | 1.011347 | 1.037183 | 1.000432 | 1.005653 | 1.002891 | 1.006597 | NaN |
| 2012 | 0.995396 | 0.982514 | 1.000151 | 0.997335 | 0.997441 | 0.998510 | NaN | 0.993732 | 0.989014 | 1.000105 | 0.998778 | 0.996178 | 0.998416 | NaN | 0.995232 | 0.982129 | 1.000141 | 0.996983 | 0.997441 | 0.998486 | NaN | 0.987609 | 0.987500 | 0.999711 | 0.994395 | 0.995681 | 0.997941 | NaN | 1.005298 | 1.024391 | 1.001971 | 1.002306 | 1.001893 | 1.002001 | NaN | 1.007272 | 1.013150 | 1.002076 | 1.001401 | 1.003314 | 1.002665 | NaN | 1.005423 | 1.024493 | 1.001983 | 1.002849 | 1.001913 | 1.002043 | NaN | 1.017422 | 1.014763 | 1.002134 | 1.005236 | 1.004770 | 1.002963 | NaN |
| 2013 | 0.997552 | 0.994285 | 0.996971 | 0.999728 | 0.998587 | 0.999346 | NaN | 0.997202 | 0.994854 | 0.997923 | 0.999652 | 0.997300 | 0.999646 | NaN | 0.997444 | 0.994234 | 0.996971 | 0.999489 | 0.998585 | 0.999339 | NaN | 0.985819 | 0.993857 | 0.997194 | 0.996086 | 0.996498 | 0.999391 | NaN | 1.000617 | 1.008389 | 1.002587 | 1.000326 | 1.001286 | 1.002257 | NaN | 1.002618 | 1.008552 | 1.000911 | 1.000352 | 1.003391 | 1.001985 | NaN | 1.000655 | 1.008450 | 1.002590 | 1.000540 | 1.001414 | 1.002271 | NaN | 1.013285 | 1.009234 | 1.000928 | 1.002497 | 1.006027 | 1.002251 | NaN |
| 2014 | 0.995700 | 0.997577 | 0.991220 | 0.999496 | 0.993239 | 0.998816 | NaN | 0.990090 | 0.999400 | 0.990672 | 0.999722 | 0.992544 | 0.999025 | NaN | 0.992633 | 0.997415 | 0.991199 | 0.999027 | 0.993239 | 0.998762 | NaN | 0.960239 | 0.998269 | 0.980942 | 0.996386 | 0.990766 | 0.997557 | NaN | 1.003777 | 1.006847 | 1.003339 | 1.000284 | 1.001075 | 1.000043 | NaN | 1.011600 | 1.002867 | 1.001802 | 1.000302 | 1.000994 | 0.999981 | NaN | 1.003891 | 1.006913 | 1.003339 | 1.000545 | 1.001075 | 1.000059 | NaN | 1.030792 | 1.003326 | 1.001974 | 1.002085 | 1.002304 | 1.000538 | NaN |
| 2015 | 0.990244 | 0.986939 | 0.996699 | 0.999311 | 0.999161 | 0.999762 | 0.994087 | 0.978461 | 0.997624 | 0.996650 | 0.999324 | 0.998190 | 0.999864 | 0.996920 | 0.987560 | 0.986779 | 0.996658 | 0.998959 | 0.999124 | 0.999710 | 0.994064 | 0.931639 | 0.994492 | 0.994545 | 0.994858 | 0.996215 | 0.997973 | 0.953715 | 1.004217 | 1.023337 | 1.002844 | 1.001603 | 1.001855 | 1.000930 | 1.002896 | 1.020153 | 1.005624 | 1.005136 | 1.001028 | 1.003692 | 1.000788 | 1.002752 | 1.004822 | 1.023361 | 1.002846 | 1.001982 | 1.001864 | 1.000953 | 1.002896 | 1.046212 | 1.006872 | 1.006976 | 1.005214 | 1.006242 | 1.002160 | 1.066360 |
| 2016 | 0.983132 | 0.992566 | 0.999431 | 0.997871 | 0.997944 | 0.998749 | 0.985828 | 0.961154 | 0.993484 | 0.996959 | 0.998613 | 0.995692 | 0.998833 | 0.970385 | 0.978965 | 0.992310 | 0.999430 | 0.997587 | 0.997890 | 0.998666 | 0.985722 | 0.866740 | 0.991067 | 0.989202 | 0.994128 | 0.991426 | 0.997813 | 0.881089 | 1.010591 | 1.009690 | 1.002196 | 1.002379 | 1.002593 | 1.002218 | 1.008413 | 1.046734 | 1.008081 | 1.006293 | 1.001751 | 1.004609 | 1.002542 | 1.034590 | 1.011377 | 1.009805 | 1.002196 | 1.002493 | 1.002609 | 1.002255 | 1.008861 | 1.103048 | 1.010248 | 1.016362 | 1.004832 | 1.009081 | 1.003270 | 1.135252 |
| 2017 | 0.934574 | 0.994648 | 0.999301 | 0.998500 | 0.999544 | 0.999214 | 0.950324 | 0.895030 | 0.994937 | 0.998242 | 0.998846 | 0.999012 | 0.998726 | 0.869636 | 0.895518 | 0.994245 | 0.999206 | 0.998117 | 0.999412 | 0.999177 | 0.891771 | 0.788514 | 0.991269 | 0.992823 | 0.995671 | 0.994513 | 0.997632 | 0.778472 | 1.043464 | 1.007203 | 1.003913 | 1.003146 | 1.002299 | 1.001424 | 1.023485 | 1.128227 | 1.006908 | 1.006121 | 1.003103 | 1.004494 | 1.002649 | 1.160020 | 1.051820 | 1.007433 | 1.003940 | 1.003329 | 1.002299 | 1.001442 | 1.031005 | 1.196672 | 1.009585 | 1.009864 | 1.005275 | 1.006725 | 1.003223 | 1.284267 |
max
| CF_OptRev/OptCF_hist,da,f,mustrun | CF_OptRev/OptCF_hist,rt,f,mustrun | CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun | CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun | Rev_OptRev/OptCF_hist,da,f,mustrun | Rev_OptRev/OptCF_hist,rt,f,mustrun | Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun | Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun | |||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2010 | 1.000885 | NaN | 1.000979 | 1.000870 | 1.001024 | 1.002165 | NaN | 1.000612 | NaN | NaN | 1.000925 | 1.001009 | 1.002160 | NaN | 1.000764 | NaN | 1.000979 | 1.000864 | 1.001024 | 1.002165 | NaN | 0.995601 | NaN | NaN | 1.000400 | 0.999439 | 1.001215 | NaN | 1.005628 | NaN | 1.003642 | 1.007356 | 1.005632 | 1.009767 | NaN | 1.036135 | NaN | NaN | 1.007889 | 1.008830 | 1.011587 | NaN | 1.009463 | NaN | 1.003642 | 1.034138 | 1.005632 | 1.092097 | NaN | 1.291673 | NaN | NaN | 1.092822 | 1.033603 | 1.460574 | NaN |
| 2011 | 1.000464 | 0.992659 | 1.000948 | 1.000680 | 1.000713 | 1.001381 | NaN | 1.001070 | 0.998827 | 1.000560 | 1.000492 | 1.000518 | 1.001151 | NaN | 0.999805 | 0.982423 | 1.000948 | 1.000680 | 1.000713 | 1.001381 | NaN | 0.961598 | 0.991314 | 0.999678 | 0.998976 | 0.999897 | 1.000382 | NaN | 1.024543 | 1.093176 | 1.002037 | 1.005043 | 1.006721 | 1.011163 | NaN | 1.024102 | 1.052069 | 1.002406 | 1.005891 | 1.007643 | 1.018701 | NaN | 1.028702 | 1.242170 | 1.002037 | 1.010022 | 1.006721 | 1.056405 | NaN | 1.131301 | 1.138445 | 1.016929 | 1.066993 | 1.016984 | 1.113820 | NaN |
| 2012 | 1.000136 | 0.994097 | 1.001589 | 1.000779 | 1.000575 | 1.000563 | NaN | 1.000158 | 0.997603 | 1.001923 | 1.000904 | 1.000542 | 1.000546 | NaN | 0.999821 | 0.994097 | 1.001589 | 1.000779 | 1.000575 | 1.000563 | NaN | 0.996629 | 0.996632 | 1.001381 | 1.000343 | 0.999772 | 1.000388 | NaN | 1.031087 | 1.134148 | 1.007462 | 1.007640 | 1.014103 | 1.009528 | NaN | 1.187379 | 1.089397 | 1.008574 | 1.007198 | 1.015180 | 1.018793 | NaN | 1.071279 | 1.514284 | 1.007462 | 1.107741 | 1.014103 | 1.042595 | NaN | 1.253264 | 1.114905 | 1.016805 | 1.371152 | 1.028847 | 1.110387 | NaN |
| 2013 | 1.000394 | 0.998447 | 0.998488 | 1.000967 | 1.002098 | 1.001326 | NaN | 1.000800 | 0.999948 | 0.999369 | 1.000720 | 1.003276 | 1.001118 | NaN | 1.000180 | 0.998447 | 0.998488 | 1.000967 | 1.002098 | 1.001326 | NaN | 0.991689 | 0.999603 | 0.998622 | 1.000233 | 1.001218 | 1.001118 | NaN | 1.021769 | 1.052779 | 1.005178 | 1.003818 | 1.006411 | 1.006031 | NaN | 1.016021 | 1.063175 | 1.003808 | 1.008644 | 1.014256 | 1.021508 | NaN | 1.021894 | 1.060958 | 1.005979 | 1.106700 | 1.006411 | 1.034658 | NaN | 1.082055 | 1.115892 | 1.004362 | 1.466804 | 1.063301 | 1.072655 | NaN |
| 2014 | 1.000641 | 1.000869 | 0.995355 | 1.000447 | 0.999747 | 1.000945 | NaN | 0.999862 | 1.001532 | 0.995070 | 1.000726 | 0.999739 | 1.000961 | NaN | 0.999313 | 1.000869 | 0.995355 | 1.000447 | 0.999747 | 1.000945 | NaN | 0.974773 | 1.000699 | 0.987913 | 1.000622 | 0.998229 | 1.000742 | NaN | 1.027635 | 1.029071 | 1.009941 | 1.015850 | 1.007347 | 1.005186 | NaN | 1.043214 | 1.028569 | 1.007982 | 1.019880 | 1.008567 | 1.004961 | NaN | 1.027648 | 1.073666 | 1.009941 | 1.065326 | 1.007347 | 1.030248 | NaN | 1.166288 | 1.059748 | 1.008139 | 1.420097 | 1.053436 | 1.150027 | NaN |
| 2015 | 0.998203 | 0.993558 | 0.999503 | 1.001492 | 1.002645 | 1.001623 | 0.999814 | 0.998154 | 1.000206 | 1.000978 | 1.000674 | 1.002823 | 1.001383 | 1.000216 | 0.998203 | 0.993558 | 0.999309 | 1.001492 | 1.002645 | 1.001623 | 0.999814 | 0.951362 | 0.999391 | 0.998863 | 1.000246 | 0.998994 | 1.001145 | 0.963234 | 1.069368 | 1.038548 | 1.014036 | 1.010807 | 1.005598 | 1.004477 | 1.012461 | 1.106972 | 1.060230 | 1.010324 | 1.031035 | 1.016377 | 1.013788 | 1.009446 | 1.069368 | 1.044821 | 1.014037 | 1.072182 | 1.005598 | 1.140447 | 1.012461 | 1.338491 | 1.068929 | 1.011737 | 1.334974 | 1.085185 | 1.266746 | 1.143931 |
| 2016 | 0.997060 | 0.996633 | 1.001765 | 1.001313 | 1.000533 | 1.001238 | 1.000592 | 0.989867 | 0.999290 | 1.001243 | 1.001278 | 1.001123 | 1.001243 | 0.999775 | 0.997060 | 0.996444 | 1.001765 | 1.001313 | 1.000436 | 1.001238 | 1.000347 | 0.898813 | 0.996503 | 0.993100 | 1.001038 | 0.998722 | 1.000344 | 0.913883 | 1.030858 | 1.041762 | 1.006216 | 1.008950 | 1.012860 | 1.009045 | 1.031370 | 1.174392 | 1.044713 | 1.014900 | 1.014916 | 1.029832 | 1.012171 | 1.120918 | 1.076988 | 1.097889 | 1.024954 | 1.023332 | 1.012860 | 1.038580 | 1.031381 | 1.443184 | 1.125101 | 1.074771 | 1.126758 | 1.261031 | 1.156913 | 1.342019 |
| 2017 | 0.987178 | 0.999898 | 1.001463 | 1.000558 | 1.001635 | 1.001686 | 0.999567 | 0.966279 | 0.999976 | 1.001362 | 1.000717 | 1.001731 | 1.001927 | 0.998234 | 0.941928 | 0.999747 | 1.001463 | 1.000558 | 1.001635 | 1.001686 | 0.948706 | 0.841168 | 0.999094 | 0.996927 | 1.000707 | 1.000436 | 1.000858 | 0.906871 | 1.222468 | 1.035323 | 1.010683 | 1.011823 | 1.005270 | 1.016667 | 1.082011 | 1.414609 | 1.035030 | 1.014126 | 1.013034 | 1.018715 | 1.033491 | 1.310973 | 1.559459 | 1.082642 | 1.192813 | 1.037185 | 1.005270 | 1.017851 | 1.086942 | 2.159810 | 1.347413 | 1.467675 | 1.247010 | 1.216046 | 1.093964 | 2.087167 |
CF_OptRev/OptCF_hist,da,f,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 0.998796 | 0.998487 | 0.994592 | 0.997408 | 0.995259 | 0.988713 | 0.982427 | 0.935713 | 0.966208 | 0.981868 | 0.993702 | 0.997210 | 0.986896 | 0.992160 | 0.994420 | 0.999888 | 1.000027 | 1.000016 | 0.996843 | 0.991127 | 0.996739 | 0.999449 | 0.999312 | 0.999154 | 0.998784 | 0.997456 | 0.999583 | 0.998737 | 0.998970 | 0.997467 | 0.997970 | 0.998194 | 0.997728 | 0.996784 | 0.998655 | 0.994335 | 0.999219 | 0.997238 | 0.999478 | 0.998691 | 0.997924 | 0.998425 | 0.999302 | 0.998204 | 0.999596 | 0.998523 | 0.999063 | 0.993927 | 0.986260 | 0.949781 |
| std | 0.000854 | 0.001168 | 0.003908 | 0.001275 | 0.002663 | 0.009309 | 0.005395 | 0.017484 | 0.005558 | 0.006738 | 0.003561 | 0.002017 | 0.002285 | 0.002210 | 0.002694 | 0.000495 | 0.000325 | 0.000778 | 0.000905 | 0.001262 | 0.001148 | 0.000744 | 0.000798 | 0.000904 | 0.000778 | 0.001326 | 0.000969 | 0.002574 | 0.001221 | 0.001947 | 0.001651 | 0.001780 | 0.001847 | 0.002881 | 0.001442 | 0.002801 | 0.001217 | 0.002325 | 0.000906 | 0.001302 | 0.001396 | 0.000758 | 0.000741 | 0.001856 | 0.000846 | 0.001122 | 0.000830 | 0.002407 | 0.005329 | 0.014940 |
| min | 0.994485 | 0.983943 | 0.970262 | 0.979743 | 0.975555 | 0.918174 | 0.954858 | 0.821078 | 0.935534 | 0.898403 | 0.959453 | 0.977599 | 0.978384 | 0.970989 | 0.974496 | 0.998227 | 0.998934 | 0.997553 | 0.993021 | 0.988006 | 0.994074 | 0.997085 | 0.996659 | 0.995694 | 0.996260 | 0.988833 | 0.991461 | 0.984061 | 0.995229 | 0.992267 | 0.993348 | 0.993677 | 0.992379 | 0.988670 | 0.994327 | 0.988252 | 0.994265 | 0.986846 | 0.996455 | 0.993131 | 0.993317 | 0.993912 | 0.996194 | 0.988883 | 0.993550 | 0.992489 | 0.991296 | 0.978781 | 0.967264 | 0.901300 |
| 2.5% | 0.997000 | 0.997062 | 0.981131 | 0.994868 | 0.988660 | 0.963387 | 0.969911 | 0.900771 | 0.956369 | 0.967658 | 0.983358 | 0.993070 | 0.982318 | 0.986915 | 0.988583 | 0.998703 | 0.999312 | 0.998202 | 0.994823 | 0.988580 | 0.994844 | 0.998000 | 0.997794 | 0.997545 | 0.996996 | 0.995249 | 0.998378 | 0.988304 | 0.995849 | 0.993325 | 0.993927 | 0.995413 | 0.994039 | 0.989467 | 0.995275 | 0.990654 | 0.996430 | 0.991622 | 0.997652 | 0.996125 | 0.994905 | 0.996680 | 0.997828 | 0.992948 | 0.997692 | 0.995987 | 0.997343 | 0.988626 | 0.976825 | 0.922247 |
| 25% | 0.998253 | 0.998202 | 0.993816 | 0.996829 | 0.994232 | 0.986914 | 0.979571 | 0.926839 | 0.962453 | 0.980647 | 0.993180 | 0.996352 | 0.985333 | 0.991407 | 0.993451 | 0.999616 | 0.999881 | 0.999613 | 0.996305 | 0.990333 | 0.995761 | 0.999011 | 0.998736 | 0.998606 | 0.998380 | 0.996779 | 0.999281 | 0.998883 | 0.998109 | 0.996101 | 0.996927 | 0.997005 | 0.996070 | 0.996785 | 0.997814 | 0.992124 | 0.998520 | 0.997359 | 0.998972 | 0.997786 | 0.996951 | 0.998009 | 0.998797 | 0.997684 | 0.999070 | 0.997846 | 0.998523 | 0.992616 | 0.982588 | 0.939064 |
| 50% | 0.998748 | 0.998575 | 0.995396 | 0.997552 | 0.995700 | 0.990244 | 0.983132 | 0.934574 | 0.965722 | 0.982514 | 0.994285 | 0.997577 | 0.986939 | 0.992566 | 0.994648 | 0.999933 | 1.000060 | 1.000151 | 0.996971 | 0.991220 | 0.996699 | 0.999431 | 0.999301 | 0.999281 | 0.998788 | 0.997335 | 0.999728 | 0.999496 | 0.999311 | 0.997871 | 0.998500 | 0.997796 | 0.997443 | 0.997441 | 0.998587 | 0.993239 | 0.999161 | 0.997944 | 0.999544 | 0.998742 | 0.998193 | 0.998510 | 0.999346 | 0.998816 | 0.999762 | 0.998749 | 0.999214 | 0.994087 | 0.985828 | 0.950324 |
| 75% | 0.999440 | 0.998987 | 0.996528 | 0.998272 | 0.996746 | 0.993780 | 0.985903 | 0.942720 | 0.969907 | 0.984632 | 0.995235 | 0.998447 | 0.988545 | 0.993382 | 0.995999 | 1.000222 | 1.000229 | 1.000569 | 0.997512 | 0.991863 | 0.997591 | 0.999891 | 0.999846 | 0.999805 | 0.999248 | 0.998428 | 1.000017 | 0.999918 | 0.999810 | 0.998924 | 0.999217 | 0.999837 | 0.999478 | 0.998629 | 0.999899 | 0.997184 | 1.000100 | 0.998523 | 1.000068 | 0.999713 | 0.998943 | 0.998920 | 0.999857 | 0.999478 | 1.000217 | 0.999322 | 0.999642 | 0.995444 | 0.989300 | 0.959381 |
| 97.5% | 1.000312 | 0.999628 | 0.999052 | 0.999455 | 0.999034 | 0.996649 | 0.991407 | 0.974518 | 0.978213 | 0.989772 | 0.996992 | 0.999742 | 0.991056 | 0.995188 | 0.998049 | 1.000753 | 1.000536 | 1.001276 | 0.998200 | 0.993754 | 0.999137 | 1.000977 | 1.001101 | 1.000710 | 1.000420 | 0.999853 | 1.000539 | 1.000287 | 1.000788 | 1.000715 | 1.000219 | 1.000829 | 1.000400 | 0.999797 | 1.001271 | 0.999528 | 1.001163 | 0.999591 | 1.001194 | 1.000874 | 1.000207 | 0.999723 | 1.000622 | 1.000029 | 1.000796 | 1.000231 | 1.000346 | 0.998098 | 0.996943 | 0.985055 |
| max | 1.000885 | 1.000464 | 1.000136 | 1.000394 | 1.000641 | 0.998203 | 0.997060 | 0.987178 | 0.992659 | 0.994097 | 0.998447 | 1.000869 | 0.993558 | 0.996633 | 0.999898 | 1.000979 | 1.000948 | 1.001589 | 0.998488 | 0.995355 | 0.999503 | 1.001765 | 1.001463 | 1.000870 | 1.000680 | 1.000779 | 1.000967 | 1.000447 | 1.001492 | 1.001313 | 1.000558 | 1.001024 | 1.000713 | 1.000575 | 1.002098 | 0.999747 | 1.002645 | 1.000533 | 1.001635 | 1.002165 | 1.001381 | 1.000563 | 1.001326 | 1.000945 | 1.001623 | 1.001238 | 1.001686 | 0.999814 | 1.000592 | 0.999567 |
CF_OptRev/OptCF_hist,rt,f,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 0.997275 | 0.997449 | 0.988007 | 0.996816 | 0.989203 | 0.974862 | 0.957673 | 0.896420 | 0.974435 | 0.987381 | 0.994134 | 0.999015 | 0.997136 | 0.992793 | 0.994352 | NaN | 0.999948 | 0.999971 | 0.997789 | 0.990734 | 0.996829 | 0.997013 | 0.998173 | 0.999351 | 0.998895 | 0.998566 | 0.999481 | 0.999168 | 0.998760 | 0.997955 | 0.998323 | 0.997386 | 0.997230 | 0.996247 | 0.997567 | 0.993552 | 0.998045 | 0.993662 | 0.998389 | 0.998847 | 0.997177 | 0.998333 | 0.999097 | 0.998500 | 0.999590 | 0.998657 | 0.998627 | 0.996846 | 0.960948 | 0.893412 |
| std | 0.002789 | 0.002262 | 0.019639 | 0.002114 | 0.005117 | 0.012789 | 0.014243 | 0.023111 | 0.006919 | 0.008153 | 0.004200 | 0.001550 | 0.002289 | 0.003465 | 0.003191 | NaN | 0.000209 | 0.000877 | 0.000799 | 0.001400 | 0.001375 | 0.001710 | 0.001211 | 0.000748 | 0.000810 | 0.001197 | 0.001061 | 0.001896 | 0.001635 | 0.002066 | 0.001624 | 0.002483 | 0.001940 | 0.002634 | 0.002147 | 0.003324 | 0.002139 | 0.005308 | 0.002227 | 0.001323 | 0.002074 | 0.000886 | 0.002469 | 0.001731 | 0.000985 | 0.001085 | 0.001023 | 0.001501 | 0.025359 | 0.058190 |
| min | 0.943378 | 0.985167 | 0.875259 | 0.981692 | 0.955062 | 0.907179 | 0.882312 | 0.774044 | 0.954631 | 0.920048 | 0.947713 | 0.981910 | 0.960395 | 0.963861 | 0.973992 | NaN | 0.999055 | 0.994092 | 0.994131 | 0.987475 | 0.993118 | 0.990631 | 0.994321 | 0.995042 | 0.995949 | 0.991274 | 0.990711 | 0.987463 | 0.989015 | 0.991452 | 0.992398 | 0.991607 | 0.990888 | 0.988671 | 0.992264 | 0.987781 | 0.989481 | 0.975938 | 0.989050 | 0.994002 | 0.989546 | 0.991290 | 0.979759 | 0.988738 | 0.983633 | 0.991613 | 0.980791 | 0.983327 | 0.905662 | 0.740967 |
| 2.5% | 0.993664 | 0.991076 | 0.916268 | 0.991084 | 0.978264 | 0.947777 | 0.924494 | 0.856013 | 0.961282 | 0.966363 | 0.984231 | 0.994886 | 0.991841 | 0.981638 | 0.986982 | NaN | 0.999572 | 0.998001 | 0.995845 | 0.988021 | 0.994274 | 0.993551 | 0.995906 | 0.998118 | 0.996962 | 0.996160 | 0.998342 | 0.992486 | 0.993467 | 0.993415 | 0.993302 | 0.993317 | 0.993386 | 0.990703 | 0.993290 | 0.989202 | 0.992397 | 0.980671 | 0.992422 | 0.996051 | 0.992427 | 0.996349 | 0.989486 | 0.994122 | 0.997226 | 0.996255 | 0.996579 | 0.993635 | 0.918875 | 0.808626 |
| 25% | 0.996473 | 0.996487 | 0.989655 | 0.995678 | 0.986987 | 0.966972 | 0.950459 | 0.883491 | 0.969665 | 0.986726 | 0.993208 | 0.998623 | 0.996762 | 0.992013 | 0.992944 | NaN | 0.999843 | 0.999486 | 0.997442 | 0.989814 | 0.995825 | 0.995789 | 0.997292 | 0.998946 | 0.998461 | 0.997943 | 0.999209 | 0.999140 | 0.998074 | 0.996690 | 0.997640 | 0.995579 | 0.995500 | 0.994592 | 0.996442 | 0.990734 | 0.997398 | 0.993299 | 0.997910 | 0.997868 | 0.995659 | 0.997892 | 0.999292 | 0.997681 | 0.999099 | 0.998053 | 0.998062 | 0.996130 | 0.938267 | 0.844219 |
| 50% | 0.997344 | 0.997797 | 0.993732 | 0.997202 | 0.990090 | 0.978461 | 0.961154 | 0.895030 | 0.974514 | 0.989014 | 0.994854 | 0.999400 | 0.997624 | 0.993484 | 0.994937 | NaN | 0.999944 | 1.000105 | 0.997923 | 0.990672 | 0.996650 | 0.996959 | 0.998242 | 0.999289 | 0.999063 | 0.998778 | 0.999652 | 0.999722 | 0.999324 | 0.998613 | 0.998846 | 0.997175 | 0.997963 | 0.996178 | 0.997300 | 0.992544 | 0.998190 | 0.995692 | 0.999012 | 0.999025 | 0.997861 | 0.998416 | 0.999646 | 0.999025 | 0.999864 | 0.998833 | 0.998726 | 0.996920 | 0.970385 | 0.869636 |
| 75% | 0.998577 | 0.998950 | 0.996423 | 0.998419 | 0.992413 | 0.984150 | 0.967370 | 0.910913 | 0.979444 | 0.991251 | 0.996110 | 0.999916 | 0.998260 | 0.994660 | 0.996325 | NaN | 1.000062 | 1.000548 | 0.998318 | 0.991639 | 0.997810 | 0.998306 | 0.998981 | 0.999833 | 0.999350 | 0.999323 | 0.999980 | 0.999973 | 0.999836 | 0.999447 | 0.999399 | 0.999703 | 0.998774 | 0.998420 | 0.999235 | 0.997115 | 0.999346 | 0.996526 | 0.999894 | 0.999906 | 0.998774 | 0.998901 | 0.999961 | 0.999788 | 1.000263 | 0.999415 | 0.999315 | 0.997965 | 0.982880 | 0.947605 |
| 97.5% | 1.000093 | 1.000247 | 0.999735 | 0.999876 | 0.996034 | 0.990318 | 0.978746 | 0.945262 | 0.987306 | 0.994515 | 0.998349 | 1.000521 | 0.999355 | 0.996653 | 0.999120 | NaN | 1.000313 | 1.001223 | 0.999057 | 0.993602 | 0.999689 | 1.000197 | 1.000670 | 1.000681 | 1.000303 | 1.000165 | 1.000381 | 1.000468 | 1.000527 | 1.000732 | 1.000340 | 1.000804 | 1.000168 | 1.000014 | 1.001749 | 0.999441 | 1.001338 | 0.999285 | 1.000857 | 1.000974 | 0.999941 | 0.999790 | 1.000485 | 1.000355 | 1.000797 | 1.000296 | 1.000181 | 0.999222 | 0.993547 | 0.988034 |
| max | 1.000612 | 1.001070 | 1.000158 | 1.000800 | 0.999862 | 0.998154 | 0.989867 | 0.966279 | 0.998827 | 0.997603 | 0.999948 | 1.001532 | 1.000206 | 0.999290 | 0.999976 | NaN | 1.000560 | 1.001923 | 0.999369 | 0.995070 | 1.000978 | 1.001243 | 1.001362 | 1.000925 | 1.000492 | 1.000904 | 1.000720 | 1.000726 | 1.000674 | 1.001278 | 1.000717 | 1.001009 | 1.000518 | 1.000542 | 1.003276 | 0.999739 | 1.002823 | 1.001123 | 1.001731 | 1.002160 | 1.001151 | 1.000546 | 1.001118 | 1.000961 | 1.001383 | 1.001243 | 1.001927 | 1.000216 | 0.999775 | 0.998234 |
CF_OptRev/OptCF_hist,da,f,curtail,baselinemustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 0.998629 | 0.997390 | 0.994000 | 0.997138 | 0.992286 | 0.986128 | 0.975258 | 0.890050 | 0.964369 | 0.981009 | 0.993507 | 0.996898 | 0.986716 | 0.991702 | 0.993368 | 0.999862 | 1.000018 | 0.999964 | 0.996817 | 0.991085 | 0.996689 | 0.999361 | 0.998683 | 0.998553 | 0.998318 | 0.994970 | 0.997186 | 0.997158 | 0.997464 | 0.996783 | 0.997201 | 0.998187 | 0.997728 | 0.996770 | 0.998432 | 0.994335 | 0.999163 | 0.997145 | 0.999443 | 0.998307 | 0.997824 | 0.998125 | 0.999179 | 0.998040 | 0.999340 | 0.998305 | 0.998911 | 0.993916 | 0.984469 | 0.894405 |
| std | 0.001072 | 0.003723 | 0.005647 | 0.001701 | 0.003015 | 0.010172 | 0.012293 | 0.028419 | 0.007056 | 0.009792 | 0.004005 | 0.002902 | 0.002410 | 0.003036 | 0.004721 | 0.000530 | 0.000348 | 0.000906 | 0.000938 | 0.001277 | 0.001140 | 0.001070 | 0.005175 | 0.003685 | 0.001755 | 0.011380 | 0.010034 | 0.007780 | 0.005624 | 0.003462 | 0.003938 | 0.001774 | 0.001847 | 0.002873 | 0.002002 | 0.002801 | 0.001270 | 0.002348 | 0.000949 | 0.003683 | 0.001936 | 0.002760 | 0.001954 | 0.002216 | 0.002457 | 0.002376 | 0.001458 | 0.002409 | 0.007164 | 0.017465 |
| min | 0.991658 | 0.927782 | 0.932775 | 0.978054 | 0.972427 | 0.918174 | 0.892388 | 0.696302 | 0.851269 | 0.714140 | 0.934740 | 0.951365 | 0.972641 | 0.954112 | 0.919628 | 0.997141 | 0.998204 | 0.992490 | 0.993021 | 0.988006 | 0.991329 | 0.982377 | 0.892283 | 0.955199 | 0.984417 | 0.871239 | 0.903485 | 0.876191 | 0.950602 | 0.952288 | 0.944006 | 0.993677 | 0.992379 | 0.988670 | 0.982667 | 0.988252 | 0.993520 | 0.986846 | 0.995336 | 0.914349 | 0.951535 | 0.927662 | 0.952675 | 0.974309 | 0.937725 | 0.953378 | 0.976430 | 0.978781 | 0.965471 | 0.813535 |
| 2.5% | 0.996368 | 0.992921 | 0.978058 | 0.991997 | 0.983390 | 0.962322 | 0.948286 | 0.815125 | 0.951304 | 0.965778 | 0.981433 | 0.991581 | 0.981741 | 0.984017 | 0.982780 | 0.998500 | 0.999187 | 0.998160 | 0.994758 | 0.988545 | 0.994800 | 0.997599 | 0.996776 | 0.993543 | 0.993509 | 0.980460 | 0.984901 | 0.985170 | 0.982680 | 0.990538 | 0.990604 | 0.995413 | 0.994039 | 0.989467 | 0.995229 | 0.990654 | 0.995253 | 0.991622 | 0.997603 | 0.995060 | 0.994722 | 0.995439 | 0.997515 | 0.991788 | 0.996536 | 0.995447 | 0.996713 | 0.988626 | 0.972109 | 0.869722 |
| 25% | 0.998131 | 0.997462 | 0.993667 | 0.996559 | 0.991110 | 0.981663 | 0.968339 | 0.885285 | 0.960942 | 0.980190 | 0.993052 | 0.996100 | 0.985154 | 0.991016 | 0.992504 | 0.999587 | 0.999869 | 0.999576 | 0.996278 | 0.990238 | 0.995746 | 0.998985 | 0.998635 | 0.998338 | 0.998205 | 0.995782 | 0.998682 | 0.997600 | 0.997524 | 0.995150 | 0.996263 | 0.997005 | 0.996070 | 0.996785 | 0.997787 | 0.992124 | 0.998507 | 0.997171 | 0.998926 | 0.997702 | 0.996888 | 0.997977 | 0.998782 | 0.997488 | 0.998964 | 0.997751 | 0.998443 | 0.992616 | 0.977810 | 0.885437 |
| 50% | 0.998659 | 0.997985 | 0.995232 | 0.997444 | 0.992633 | 0.987560 | 0.978965 | 0.895518 | 0.964373 | 0.982129 | 0.994234 | 0.997415 | 0.986779 | 0.992310 | 0.994245 | 0.999920 | 1.000053 | 1.000141 | 0.996971 | 0.991199 | 0.996658 | 0.999430 | 0.999206 | 0.999082 | 0.998698 | 0.996983 | 0.999489 | 0.999027 | 0.998959 | 0.997587 | 0.998117 | 0.997796 | 0.997443 | 0.997441 | 0.998585 | 0.993239 | 0.999124 | 0.997890 | 0.999412 | 0.998638 | 0.998148 | 0.998486 | 0.999339 | 0.998762 | 0.999710 | 0.998666 | 0.999177 | 0.994064 | 0.985722 | 0.891771 |
| 75% | 0.999351 | 0.998415 | 0.996416 | 0.998189 | 0.993806 | 0.993375 | 0.984364 | 0.904803 | 0.968621 | 0.984177 | 0.995199 | 0.998397 | 0.988404 | 0.993260 | 0.995669 | 1.000197 | 1.000224 | 1.000567 | 0.997486 | 0.991841 | 0.997546 | 0.999890 | 0.999831 | 0.999684 | 0.999107 | 0.997671 | 0.999893 | 0.999741 | 0.999767 | 0.998694 | 0.999063 | 0.999834 | 0.999478 | 0.998626 | 0.999770 | 0.997184 | 1.000098 | 0.998523 | 1.000068 | 0.999678 | 0.998920 | 0.998914 | 0.999848 | 0.999454 | 1.000195 | 0.999301 | 0.999632 | 0.995435 | 0.989151 | 0.904210 |
| 97.5% | 1.000266 | 0.999162 | 0.998796 | 0.999414 | 0.996629 | 0.996366 | 0.990641 | 0.930582 | 0.975757 | 0.988432 | 0.996952 | 0.999735 | 0.990829 | 0.995007 | 0.997564 | 1.000753 | 1.000536 | 1.001276 | 0.998200 | 0.993754 | 0.999055 | 1.000947 | 1.001101 | 1.000697 | 1.000420 | 0.999584 | 1.000333 | 1.000257 | 1.000765 | 1.000715 | 1.000026 | 1.000829 | 1.000400 | 0.999797 | 1.001271 | 0.999528 | 1.001163 | 0.999550 | 1.001194 | 1.000868 | 1.000205 | 0.999690 | 1.000614 | 1.000028 | 1.000778 | 1.000213 | 1.000342 | 0.998098 | 0.996604 | 0.931223 |
| max | 1.000764 | 0.999805 | 0.999821 | 1.000180 | 0.999313 | 0.998203 | 0.997060 | 0.941928 | 0.982423 | 0.994097 | 0.998447 | 1.000869 | 0.993558 | 0.996444 | 0.999747 | 1.000979 | 1.000948 | 1.001589 | 0.998488 | 0.995355 | 0.999309 | 1.001765 | 1.001463 | 1.000864 | 1.000680 | 1.000779 | 1.000967 | 1.000447 | 1.001492 | 1.001313 | 1.000558 | 1.001024 | 1.000713 | 1.000575 | 1.002098 | 0.999747 | 1.002645 | 1.000436 | 1.001635 | 1.002165 | 1.001381 | 1.000563 | 1.001326 | 1.000945 | 1.001623 | 1.001238 | 1.001686 | 0.999814 | 1.000347 | 0.948706 |
CF_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 0.990817 | 0.952255 | 0.980815 | 0.984604 | 0.955739 | 0.903383 | 0.851808 | 0.781320 | 0.961689 | 0.979910 | 0.992010 | 0.997348 | 0.993827 | 0.987737 | 0.984961 | NaN | 0.999002 | 0.999486 | 0.996875 | 0.981360 | 0.994613 | 0.988500 | 0.990158 | 0.990829 | 0.988374 | 0.988810 | 0.988125 | 0.992211 | 0.991848 | 0.991987 | 0.993458 | 0.994932 | 0.995900 | 0.995231 | 0.994320 | 0.990935 | 0.993035 | 0.983676 | 0.990594 | 0.995781 | 0.994630 | 0.996855 | 0.998382 | 0.996394 | 0.996250 | 0.996467 | 0.996962 | 0.952964 | 0.867596 | 0.798585 |
| std | 0.010060 | 0.007122 | 0.020121 | 0.006498 | 0.011120 | 0.041499 | 0.032560 | 0.028541 | 0.031552 | 0.021080 | 0.006993 | 0.003822 | 0.003571 | 0.008086 | 0.016509 | NaN | 0.000460 | 0.001005 | 0.001893 | 0.002080 | 0.001418 | 0.003542 | 0.011083 | 0.009833 | 0.014072 | 0.017419 | 0.021684 | 0.015352 | 0.011226 | 0.008623 | 0.007472 | 0.001864 | 0.002092 | 0.002598 | 0.010776 | 0.005830 | 0.009098 | 0.016718 | 0.014225 | 0.005614 | 0.003748 | 0.005689 | 0.003568 | 0.003932 | 0.006920 | 0.005417 | 0.002960 | 0.004693 | 0.032158 | 0.052130 |
| min | 0.764028 | 0.911917 | 0.866171 | 0.949298 | 0.909114 | 0.810432 | 0.716126 | 0.640909 | 0.821193 | 0.772478 | 0.935146 | 0.952453 | 0.959092 | 0.949335 | 0.705733 | NaN | 0.996584 | 0.989940 | 0.981283 | 0.977059 | 0.991058 | 0.957374 | 0.900400 | 0.953892 | 0.916573 | 0.835960 | 0.821708 | 0.799411 | 0.911552 | 0.923591 | 0.939714 | 0.990161 | 0.986220 | 0.987253 | 0.926045 | 0.954808 | 0.947325 | 0.921824 | 0.926874 | 0.914513 | 0.959074 | 0.921996 | 0.952496 | 0.941077 | 0.907448 | 0.932611 | 0.967896 | 0.928684 | 0.795181 | 0.670143 |
| 2.5% | 0.985821 | 0.929036 | 0.904338 | 0.965369 | 0.931191 | 0.829372 | 0.785184 | 0.715304 | 0.861428 | 0.917689 | 0.969437 | 0.988715 | 0.985665 | 0.965863 | 0.947622 | NaN | 0.997542 | 0.997498 | 0.993178 | 0.978098 | 0.991879 | 0.977712 | 0.957133 | 0.964715 | 0.947571 | 0.947620 | 0.918438 | 0.964625 | 0.957470 | 0.970087 | 0.970303 | 0.991627 | 0.991845 | 0.989172 | 0.938445 | 0.967902 | 0.951092 | 0.930942 | 0.931704 | 0.974768 | 0.985604 | 0.987598 | 0.987018 | 0.986340 | 0.982323 | 0.987011 | 0.987555 | 0.941011 | 0.807784 | 0.728264 |
| 25% | 0.990052 | 0.950753 | 0.980286 | 0.982307 | 0.948887 | 0.859286 | 0.821576 | 0.765099 | 0.964506 | 0.982792 | 0.991720 | 0.997276 | 0.992173 | 0.985947 | 0.986380 | NaN | 0.998852 | 0.998981 | 0.996714 | 0.979981 | 0.993582 | 0.987853 | 0.991263 | 0.987115 | 0.987556 | 0.989107 | 0.989000 | 0.992413 | 0.990692 | 0.990285 | 0.992165 | 0.993531 | 0.994774 | 0.993565 | 0.994597 | 0.989795 | 0.993608 | 0.981508 | 0.990669 | 0.995127 | 0.993202 | 0.997011 | 0.998701 | 0.994922 | 0.995433 | 0.995943 | 0.996235 | 0.950280 | 0.852113 | 0.755940 |
| 50% | 0.991157 | 0.954237 | 0.987609 | 0.985819 | 0.960239 | 0.931639 | 0.866740 | 0.788514 | 0.971661 | 0.987500 | 0.993857 | 0.998269 | 0.994492 | 0.991067 | 0.991269 | NaN | 0.999117 | 0.999711 | 0.997194 | 0.980942 | 0.994545 | 0.989202 | 0.992823 | 0.994048 | 0.993504 | 0.994395 | 0.996086 | 0.996386 | 0.994858 | 0.994128 | 0.995671 | 0.995121 | 0.995763 | 0.995681 | 0.996498 | 0.990766 | 0.996215 | 0.991426 | 0.994513 | 0.997210 | 0.994846 | 0.997941 | 0.999391 | 0.997557 | 0.997973 | 0.997813 | 0.997632 | 0.953715 | 0.881089 | 0.778472 |
| 75% | 0.993029 | 0.956934 | 0.990423 | 0.988375 | 0.964902 | 0.939327 | 0.879409 | 0.803383 | 0.976843 | 0.990240 | 0.995569 | 0.998907 | 0.996185 | 0.992682 | 0.993168 | NaN | 0.999286 | 1.000147 | 0.997672 | 0.982313 | 0.995689 | 0.990377 | 0.993932 | 0.998081 | 0.996304 | 0.997284 | 0.998927 | 0.998411 | 0.998948 | 0.997054 | 0.997695 | 0.996100 | 0.997833 | 0.997238 | 0.997429 | 0.993578 | 0.997107 | 0.994632 | 0.997750 | 0.998587 | 0.997122 | 0.998570 | 0.999814 | 0.998798 | 0.999152 | 0.998695 | 0.998677 | 0.956702 | 0.890817 | 0.839451 |
| 97.5% | 0.994753 | 0.960072 | 0.995376 | 0.991029 | 0.968144 | 0.945681 | 0.890594 | 0.817736 | 0.986346 | 0.994031 | 0.997467 | 0.999727 | 0.998498 | 0.994711 | 0.995514 | NaN | 0.999565 | 1.000852 | 0.998424 | 0.986226 | 0.997424 | 0.992078 | 0.995877 | 0.999423 | 0.998272 | 0.999267 | 1.000095 | 0.999846 | 0.999881 | 1.000189 | 0.999570 | 0.998533 | 0.998828 | 0.998990 | 0.999102 | 0.997824 | 0.998225 | 0.996602 | 0.999111 | 1.000316 | 0.999139 | 0.999450 | 1.000386 | 0.999927 | 1.000349 | 0.999725 | 0.999857 | 0.959228 | 0.902796 | 0.888745 |
| max | 0.995601 | 0.961598 | 0.996629 | 0.991689 | 0.974773 | 0.951362 | 0.898813 | 0.841168 | 0.991314 | 0.996632 | 0.999603 | 1.000699 | 0.999391 | 0.996503 | 0.999094 | NaN | 0.999678 | 1.001381 | 0.998622 | 0.987913 | 0.998863 | 0.993100 | 0.996927 | 1.000400 | 0.998976 | 1.000343 | 1.000233 | 1.000622 | 1.000246 | 1.001038 | 1.000707 | 0.999439 | 0.999897 | 0.999772 | 1.001218 | 0.998229 | 0.998994 | 0.998722 | 1.000436 | 1.001215 | 1.000382 | 1.000388 | 1.001118 | 1.000742 | 1.001145 | 1.000344 | 1.000858 | 0.963234 | 0.913883 | 0.906871 |
Rev_OptRev/OptCF_hist,da,f,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.001344 | 1.001853 | 1.006515 | 1.000761 | 1.004292 | 1.005681 | 1.010948 | 1.042364 | 1.050829 | 1.024779 | 1.008881 | 1.007083 | 1.023438 | 1.010143 | 1.008054 | 1.001691 | 1.000690 | 1.002030 | 1.002555 | 1.003705 | 1.003073 | 1.002293 | 1.004049 | 1.002629 | 1.002438 | 1.002602 | 1.000488 | 1.001315 | 1.002548 | 1.002628 | 1.003720 | 1.002020 | 1.001690 | 1.003215 | 1.001616 | 1.001882 | 1.002079 | 1.003215 | 1.002330 | 1.003344 | 1.004005 | 1.002209 | 1.002406 | 1.000175 | 1.000910 | 1.002366 | 1.001558 | 1.003008 | 1.008821 | 1.025389 |
| std | 0.000871 | 0.002095 | 0.004925 | 0.001000 | 0.003049 | 0.007238 | 0.004669 | 0.018882 | 0.007012 | 0.008004 | 0.004071 | 0.002174 | 0.002952 | 0.003213 | 0.003899 | 0.000649 | 0.000362 | 0.000912 | 0.000969 | 0.001840 | 0.001587 | 0.000970 | 0.001074 | 0.001411 | 0.001313 | 0.001640 | 0.000574 | 0.003011 | 0.002481 | 0.001776 | 0.002665 | 0.001371 | 0.001625 | 0.003684 | 0.001546 | 0.002154 | 0.001338 | 0.002580 | 0.001201 | 0.001237 | 0.001327 | 0.001157 | 0.001086 | 0.000788 | 0.000835 | 0.001277 | 0.000916 | 0.001538 | 0.004209 | 0.016033 |
| min | 0.999336 | 0.999226 | 0.999689 | 0.998285 | 0.998917 | 0.997706 | 0.996916 | 0.993515 | 1.003153 | 1.005186 | 1.003081 | 1.000202 | 1.013966 | 1.003978 | 1.000095 | 1.000307 | 0.999594 | 1.000015 | 0.998162 | 0.996743 | 0.998383 | 0.999769 | 0.998923 | 0.999403 | 0.999115 | 0.999094 | 0.999798 | 0.997854 | 0.999172 | 0.999546 | 0.998966 | 1.000054 | 0.999638 | 0.999541 | 0.999759 | 0.997136 | 0.999879 | 0.999760 | 1.000064 | 0.999897 | 0.999915 | 0.999678 | 0.999303 | 0.995723 | 0.997732 | 0.999648 | 0.999460 | 0.998515 | 0.997383 | 0.988532 |
| 2.5% | 0.999825 | 0.999891 | 1.001293 | 0.999345 | 1.000983 | 0.999173 | 1.002508 | 1.005221 | 1.034801 | 1.013942 | 1.005134 | 1.004002 | 1.018019 | 1.005743 | 1.003525 | 1.000505 | 0.999929 | 1.000419 | 0.999268 | 1.000220 | 1.000878 | 1.000607 | 1.002386 | 1.000107 | 0.999978 | 1.000011 | 0.999837 | 0.999490 | 0.999907 | 0.999878 | 0.999524 | 1.000227 | 0.999835 | 1.000058 | 0.999947 | 0.998366 | 1.000196 | 1.000214 | 1.000630 | 1.001007 | 1.001696 | 1.000573 | 1.000551 | 0.999023 | 0.999266 | 1.000456 | 1.000233 | 1.000517 | 1.001328 | 1.000473 |
| 25% | 1.000710 | 1.001041 | 1.003850 | 1.000210 | 1.002496 | 1.002432 | 1.007936 | 1.033260 | 1.047552 | 1.022108 | 1.007421 | 1.005919 | 1.021463 | 1.008484 | 1.005990 | 1.001230 | 1.000531 | 1.001547 | 1.002129 | 1.002628 | 1.002045 | 1.001639 | 1.003412 | 1.001675 | 1.001391 | 1.001353 | 1.000129 | 0.999957 | 1.000752 | 1.001450 | 1.001902 | 1.000954 | 1.000534 | 1.000857 | 1.000396 | 1.000443 | 1.000803 | 1.001317 | 1.001200 | 1.002499 | 1.003091 | 1.001425 | 1.001596 | 0.999800 | 1.000339 | 1.001374 | 1.001010 | 1.002091 | 1.006288 | 1.012867 |
| 50% | 1.001266 | 1.001628 | 1.005298 | 1.000617 | 1.003777 | 1.004217 | 1.010591 | 1.043464 | 1.051259 | 1.024391 | 1.008389 | 1.006847 | 1.023337 | 1.009690 | 1.007203 | 1.001588 | 1.000664 | 1.001971 | 1.002587 | 1.003339 | 1.002844 | 1.002196 | 1.003913 | 1.002546 | 1.002493 | 1.002306 | 1.000326 | 1.000284 | 1.001603 | 1.002379 | 1.003146 | 1.001635 | 1.001098 | 1.001893 | 1.001286 | 1.001075 | 1.001855 | 1.002593 | 1.002299 | 1.003360 | 1.003984 | 1.002001 | 1.002257 | 1.000043 | 1.000930 | 1.002218 | 1.001424 | 1.002896 | 1.008413 | 1.023485 |
| 75% | 1.001889 | 1.002258 | 1.006884 | 1.001184 | 1.005314 | 1.006613 | 1.013577 | 1.052679 | 1.054676 | 1.026336 | 1.009224 | 1.007853 | 1.025263 | 1.011082 | 1.008563 | 1.002116 | 1.000897 | 1.002434 | 1.003068 | 1.004712 | 1.003708 | 1.002819 | 1.004573 | 1.003503 | 1.003377 | 1.003758 | 1.000727 | 1.000875 | 1.003693 | 1.003536 | 1.004808 | 1.002836 | 1.002327 | 1.002693 | 1.001980 | 1.003592 | 1.003151 | 1.003601 | 1.003278 | 1.004109 | 1.004755 | 1.002756 | 1.003178 | 1.000357 | 1.001426 | 1.003158 | 1.001962 | 1.003685 | 1.011032 | 1.036245 |
| 97.5% | 1.003143 | 1.004323 | 1.023274 | 1.002684 | 1.013509 | 1.019296 | 1.021914 | 1.077262 | 1.063472 | 1.039740 | 1.019471 | 1.011714 | 1.029595 | 1.019061 | 1.018603 | 1.002875 | 1.001463 | 1.004832 | 1.004290 | 1.007505 | 1.006097 | 1.004551 | 1.006162 | 1.005091 | 1.004752 | 1.006246 | 1.001552 | 1.012609 | 1.009159 | 1.007292 | 1.010264 | 1.004957 | 1.005996 | 1.012651 | 1.005368 | 1.006210 | 1.005276 | 1.009954 | 1.004332 | 1.005922 | 1.006750 | 1.005088 | 1.004629 | 1.002516 | 1.002597 | 1.005262 | 1.003452 | 1.006160 | 1.018403 | 1.059692 |
| max | 1.005628 | 1.024543 | 1.031087 | 1.021769 | 1.027635 | 1.069368 | 1.030858 | 1.222468 | 1.093176 | 1.134148 | 1.052779 | 1.029071 | 1.038548 | 1.041762 | 1.035323 | 1.003642 | 1.002037 | 1.007462 | 1.005178 | 1.009941 | 1.014036 | 1.006216 | 1.010683 | 1.007356 | 1.005043 | 1.007640 | 1.003818 | 1.015850 | 1.010807 | 1.008950 | 1.011823 | 1.005632 | 1.006721 | 1.014103 | 1.006411 | 1.007347 | 1.005598 | 1.012860 | 1.005270 | 1.009767 | 1.011163 | 1.009528 | 1.006031 | 1.005186 | 1.004477 | 1.009045 | 1.016667 | 1.012461 | 1.031370 | 1.082011 |
Rev_OptRev/OptCF_hist,rt,f,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.002688 | 1.003360 | 1.016450 | 1.003037 | 1.012454 | 1.024681 | 1.053300 | 1.130232 | 1.033844 | 1.014690 | 1.008818 | 1.003079 | 1.006100 | 1.009235 | 1.007866 | NaN | 1.000499 | 1.002215 | 1.001037 | 1.002254 | 1.005186 | 1.006589 | 1.006313 | 1.002142 | 1.002381 | 1.001636 | 1.000513 | 1.001315 | 1.003168 | 1.002295 | 1.003754 | 1.003235 | 1.002046 | 1.004041 | 1.004510 | 1.002122 | 1.005287 | 1.007832 | 1.005371 | 1.003344 | 1.005915 | 1.002871 | 1.002802 | 1.000010 | 1.000881 | 1.002822 | 1.002938 | 1.002728 | 1.044920 | 1.122097 |
| std | 0.002277 | 0.003423 | 0.029898 | 0.002270 | 0.004969 | 0.015200 | 0.021244 | 0.039487 | 0.007243 | 0.008238 | 0.004198 | 0.002073 | 0.003525 | 0.005089 | 0.004615 | NaN | 0.000466 | 0.001075 | 0.000878 | 0.001970 | 0.001718 | 0.002301 | 0.001543 | 0.001463 | 0.001413 | 0.001301 | 0.001024 | 0.003461 | 0.005078 | 0.002436 | 0.002915 | 0.002254 | 0.001557 | 0.003722 | 0.003256 | 0.002319 | 0.004017 | 0.007522 | 0.004157 | 0.001387 | 0.002255 | 0.001351 | 0.003113 | 0.000793 | 0.000891 | 0.001644 | 0.001760 | 0.001688 | 0.030577 | 0.077523 |
| min | 0.998308 | 0.998994 | 0.997390 | 0.999009 | 1.001471 | 0.999447 | 1.013205 | 1.032940 | 1.004131 | 1.002671 | 1.000497 | 0.993792 | 0.992758 | 0.999948 | 0.997546 | NaN | 0.999817 | 1.000013 | 0.997450 | 0.995219 | 1.001508 | 1.001294 | 0.999804 | 0.999461 | 0.999000 | 0.998991 | 0.999453 | 0.998649 | 0.998124 | 0.998754 | 0.999393 | 1.000035 | 0.998343 | 0.998689 | 0.999806 | 0.996553 | 0.998025 | 0.998131 | 1.000040 | 0.998348 | 1.000146 | 0.999791 | 0.999666 | 0.995498 | 0.998118 | 0.999146 | 0.997328 | 0.998984 | 0.996998 | 0.993048 |
| 2.5% | 0.999760 | 0.999770 | 1.000962 | 1.000056 | 1.005265 | 1.005476 | 1.024108 | 1.058584 | 1.018466 | 1.006874 | 1.002641 | 0.999879 | 1.002680 | 1.003665 | 1.001905 | NaN | 0.999924 | 1.000691 | 0.998724 | 0.998729 | 1.002264 | 1.003184 | 1.003646 | 0.999999 | 0.999868 | 0.999732 | 0.999655 | 0.999264 | 0.999640 | 0.999614 | 0.999799 | 1.000581 | 0.999723 | 0.999773 | 0.999999 | 0.998573 | 0.999990 | 0.999067 | 1.000695 | 1.001065 | 1.002433 | 1.000969 | 1.000358 | 0.998566 | 0.999636 | 1.000366 | 1.000684 | 0.999998 | 1.005168 | 1.004913 |
| 25% | 1.001406 | 1.001234 | 1.005282 | 1.001578 | 1.009397 | 1.013718 | 1.038686 | 1.110689 | 1.029160 | 1.011317 | 1.007401 | 1.002084 | 1.004502 | 1.006430 | 1.005478 | NaN | 1.000173 | 1.001597 | 1.000601 | 1.001001 | 1.004043 | 1.004831 | 1.005338 | 1.001112 | 1.001332 | 1.000644 | 1.000048 | 1.000031 | 1.000179 | 1.000859 | 1.001812 | 1.001562 | 1.000972 | 1.000934 | 1.002257 | 1.000542 | 1.002501 | 1.002978 | 1.002136 | 1.002473 | 1.004378 | 1.002023 | 1.001019 | 0.999654 | 1.000340 | 1.001598 | 1.001845 | 1.001378 | 1.014596 | 1.042818 |
| 50% | 1.002519 | 1.002440 | 1.007272 | 1.002618 | 1.011600 | 1.020153 | 1.046734 | 1.128227 | 1.034713 | 1.013150 | 1.008552 | 1.002867 | 1.005624 | 1.008081 | 1.006908 | NaN | 1.000401 | 1.002076 | 1.000911 | 1.001802 | 1.005136 | 1.006293 | 1.006121 | 1.002033 | 1.002398 | 1.001401 | 1.000352 | 1.000302 | 1.001028 | 1.001751 | 1.003103 | 1.002441 | 1.001928 | 1.003314 | 1.003391 | 1.000994 | 1.003692 | 1.004609 | 1.004494 | 1.003203 | 1.005683 | 1.002665 | 1.001985 | 0.999981 | 1.000788 | 1.002542 | 1.002649 | 1.002752 | 1.034590 | 1.160020 |
| 75% | 1.003555 | 1.004238 | 1.010560 | 1.003988 | 1.014891 | 1.032562 | 1.065843 | 1.146930 | 1.038690 | 1.014868 | 1.009712 | 1.003852 | 1.006750 | 1.010144 | 1.008694 | NaN | 1.000692 | 1.002603 | 1.001488 | 1.003443 | 1.006125 | 1.008043 | 1.007150 | 1.002930 | 1.003543 | 1.002180 | 1.000643 | 1.000782 | 1.004753 | 1.002967 | 1.004878 | 1.004151 | 1.002597 | 1.005146 | 1.006377 | 1.003911 | 1.008984 | 1.009309 | 1.006155 | 1.004034 | 1.007213 | 1.003422 | 1.003323 | 1.000272 | 1.001230 | 1.003733 | 1.003642 | 1.003643 | 1.073992 | 1.185047 |
| 97.5% | 1.006609 | 1.013850 | 1.126914 | 1.008574 | 1.022666 | 1.056382 | 1.099080 | 1.202694 | 1.046780 | 1.035964 | 1.016889 | 1.007617 | 1.014883 | 1.025347 | 1.021794 | NaN | 1.001540 | 1.006213 | 1.003091 | 1.006272 | 1.008733 | 1.011876 | 1.009868 | 1.005508 | 1.005001 | 1.004264 | 1.001761 | 1.015269 | 1.016112 | 1.008465 | 1.011534 | 1.007728 | 1.006544 | 1.013541 | 1.012792 | 1.006570 | 1.014854 | 1.026446 | 1.016883 | 1.006508 | 1.011363 | 1.005812 | 1.014042 | 1.002046 | 1.002981 | 1.006591 | 1.006800 | 1.006825 | 1.092552 | 1.226043 |
| max | 1.036135 | 1.024102 | 1.187379 | 1.016021 | 1.043214 | 1.106972 | 1.174392 | 1.414609 | 1.052069 | 1.089397 | 1.063175 | 1.028569 | 1.060230 | 1.044713 | 1.035030 | NaN | 1.002406 | 1.008574 | 1.003808 | 1.007982 | 1.010324 | 1.014900 | 1.014126 | 1.007889 | 1.005891 | 1.007198 | 1.008644 | 1.019880 | 1.031035 | 1.014916 | 1.013034 | 1.008830 | 1.007643 | 1.015180 | 1.014256 | 1.008567 | 1.016377 | 1.029832 | 1.018715 | 1.011587 | 1.018701 | 1.018793 | 1.021508 | 1.004961 | 1.013788 | 1.012171 | 1.033491 | 1.009446 | 1.120918 | 1.310973 |
Rev_OptRev/OptCF_hist,da,f,curtail,baselinemustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.001415 | 1.002330 | 1.007064 | 1.000855 | 1.004452 | 1.006482 | 1.012630 | 1.053642 | 1.051509 | 1.025275 | 1.008982 | 1.007456 | 1.023552 | 1.010702 | 1.008680 | 1.001697 | 1.000690 | 1.002039 | 1.002566 | 1.003711 | 1.003099 | 1.002323 | 1.004324 | 1.002925 | 1.002536 | 1.004237 | 1.002429 | 1.002158 | 1.003548 | 1.002964 | 1.004263 | 1.002021 | 1.001690 | 1.003222 | 1.001684 | 1.001882 | 1.002096 | 1.003229 | 1.002337 | 1.003567 | 1.004073 | 1.002340 | 1.002476 | 1.000266 | 1.001081 | 1.002465 | 1.001630 | 1.003009 | 1.009095 | 1.034967 |
| std | 0.001027 | 0.002871 | 0.006460 | 0.001174 | 0.003177 | 0.007640 | 0.006799 | 0.033171 | 0.009838 | 0.014526 | 0.004212 | 0.003820 | 0.003051 | 0.005241 | 0.005074 | 0.000648 | 0.000362 | 0.000910 | 0.000978 | 0.001836 | 0.001659 | 0.001251 | 0.006241 | 0.002824 | 0.001435 | 0.010374 | 0.011252 | 0.005012 | 0.006355 | 0.002488 | 0.003873 | 0.001370 | 0.001625 | 0.003682 | 0.001536 | 0.002154 | 0.001342 | 0.002594 | 0.001205 | 0.003440 | 0.001916 | 0.001854 | 0.001466 | 0.001148 | 0.002728 | 0.001655 | 0.001117 | 0.001537 | 0.004157 | 0.014447 |
| min | 0.999341 | 0.999243 | 0.999689 | 0.998285 | 0.998932 | 0.997738 | 0.996916 | 0.998301 | 1.012164 | 1.005186 | 1.003383 | 1.000202 | 1.013966 | 1.004242 | 1.001092 | 1.000307 | 0.999594 | 1.000015 | 0.998162 | 0.996743 | 0.998385 | 0.999769 | 1.001323 | 0.999403 | 0.999115 | 0.999094 | 0.999827 | 0.997854 | 0.999172 | 0.999574 | 0.999325 | 1.000054 | 0.999638 | 0.999541 | 0.999759 | 0.997136 | 0.999879 | 0.999766 | 1.000064 | 1.000039 | 0.999915 | 0.999678 | 0.999303 | 0.995723 | 0.997732 | 0.999648 | 0.999461 | 0.998515 | 0.997639 | 0.999390 |
| 2.5% | 0.999826 | 0.999906 | 1.001843 | 0.999345 | 1.000990 | 0.999650 | 1.002850 | 1.010603 | 1.035433 | 1.015066 | 1.005142 | 1.004105 | 1.018189 | 1.005929 | 1.004003 | 1.000505 | 0.999929 | 1.000419 | 0.999268 | 1.000220 | 1.000880 | 1.000607 | 1.002390 | 1.000107 | 1.000011 | 1.000102 | 0.999864 | 0.999528 | 0.999956 | 1.000053 | 0.999600 | 1.000227 | 0.999835 | 1.000058 | 0.999947 | 0.998366 | 1.000231 | 1.000214 | 1.000630 | 1.001025 | 1.001726 | 1.000594 | 1.000551 | 0.999029 | 0.999273 | 1.000458 | 1.000267 | 1.000517 | 1.001328 | 1.009892 |
| 25% | 1.000717 | 1.001138 | 1.004035 | 1.000221 | 1.002594 | 1.002809 | 1.008826 | 1.041737 | 1.047873 | 1.022309 | 1.007484 | 1.005961 | 1.021498 | 1.008543 | 1.006188 | 1.001236 | 1.000531 | 1.001547 | 1.002142 | 1.002655 | 1.002046 | 1.001639 | 1.003414 | 1.001740 | 1.001595 | 1.001640 | 1.000184 | 1.000013 | 1.000872 | 1.001570 | 1.002024 | 1.000954 | 1.000534 | 1.000857 | 1.000544 | 1.000443 | 1.000852 | 1.001319 | 1.001200 | 1.002550 | 1.003144 | 1.001455 | 1.001611 | 0.999815 | 1.000351 | 1.001398 | 1.001031 | 1.002091 | 1.006767 | 1.025974 |
| 50% | 1.001283 | 1.001747 | 1.005423 | 1.000655 | 1.003891 | 1.004822 | 1.011377 | 1.051820 | 1.051538 | 1.024493 | 1.008450 | 1.006913 | 1.023361 | 1.009805 | 1.007433 | 1.001588 | 1.000665 | 1.001983 | 1.002590 | 1.003339 | 1.002846 | 1.002196 | 1.003940 | 1.002617 | 1.002526 | 1.002849 | 1.000540 | 1.000545 | 1.001982 | 1.002493 | 1.003329 | 1.001635 | 1.001098 | 1.001913 | 1.001414 | 1.001075 | 1.001864 | 1.002609 | 1.002299 | 1.003387 | 1.004008 | 1.002043 | 1.002271 | 1.000059 | 1.000953 | 1.002255 | 1.001442 | 1.002896 | 1.008861 | 1.031005 |
| 75% | 1.001979 | 1.002473 | 1.007019 | 1.001233 | 1.005477 | 1.007494 | 1.014976 | 1.060932 | 1.054878 | 1.026484 | 1.009337 | 1.008087 | 1.025315 | 1.011196 | 1.008995 | 1.002116 | 1.000897 | 1.002446 | 1.003103 | 1.004712 | 1.003709 | 1.002832 | 1.004579 | 1.003680 | 1.003378 | 1.004126 | 1.001028 | 1.001573 | 1.004802 | 1.003625 | 1.005394 | 1.002836 | 1.002327 | 1.002693 | 1.002113 | 1.003592 | 1.003151 | 1.003601 | 1.003337 | 1.004155 | 1.004775 | 1.002831 | 1.003206 | 1.000403 | 1.001461 | 1.003213 | 1.002012 | 1.003685 | 1.011118 | 1.043570 |
| 97.5% | 1.003483 | 1.010954 | 1.026204 | 1.003323 | 1.014081 | 1.026925 | 1.030293 | 1.110678 | 1.063882 | 1.041061 | 1.020644 | 1.014280 | 1.029847 | 1.022904 | 1.020938 | 1.002875 | 1.001463 | 1.004832 | 1.004301 | 1.007505 | 1.006232 | 1.004624 | 1.006169 | 1.006507 | 1.004767 | 1.007940 | 1.012093 | 1.014245 | 1.010807 | 1.008263 | 1.011443 | 1.004957 | 1.005996 | 1.012651 | 1.005368 | 1.006210 | 1.005276 | 1.009954 | 1.004332 | 1.006397 | 1.006837 | 1.005334 | 1.004731 | 1.003178 | 1.002950 | 1.005714 | 1.003946 | 1.006160 | 1.018514 | 1.067021 |
| max | 1.009463 | 1.028702 | 1.071279 | 1.021894 | 1.027648 | 1.069368 | 1.076988 | 1.559459 | 1.242170 | 1.514284 | 1.060958 | 1.073666 | 1.044821 | 1.097889 | 1.082642 | 1.003642 | 1.002037 | 1.007462 | 1.005979 | 1.009941 | 1.014037 | 1.024954 | 1.192813 | 1.034138 | 1.010022 | 1.107741 | 1.106700 | 1.065326 | 1.072182 | 1.023332 | 1.037185 | 1.005632 | 1.006721 | 1.014103 | 1.006411 | 1.007347 | 1.005598 | 1.012860 | 1.005270 | 1.092097 | 1.056405 | 1.042595 | 1.034658 | 1.030248 | 1.140447 | 1.038580 | 1.017851 | 1.012461 | 1.031381 | 1.086942 |
Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.006924 | 1.017260 | 1.034871 | 1.016763 | 1.038748 | 1.102288 | 1.124199 | 1.224193 | 1.038122 | 1.018380 | 1.010248 | 1.004491 | 1.008869 | 1.012915 | 1.014924 | NaN | 1.000705 | 1.002337 | 1.001078 | 1.002446 | 1.007051 | 1.017867 | 1.014087 | 1.009200 | 1.009378 | 1.013954 | 1.014917 | 1.010220 | 1.011809 | 1.009344 | 1.010108 | 1.008013 | 1.003729 | 1.005993 | 1.007946 | 1.004165 | 1.008800 | 1.025230 | 1.018216 | 1.005897 | 1.007085 | 1.003903 | 1.003440 | 1.001905 | 1.004350 | 1.005058 | 1.005149 | 1.069101 | 1.141030 | 1.293522 |
| std | 0.013094 | 0.017952 | 0.038904 | 0.011475 | 0.017882 | 0.079572 | 0.048512 | 0.101965 | 0.009986 | 0.011168 | 0.006423 | 0.005160 | 0.007187 | 0.008909 | 0.017603 | NaN | 0.001537 | 0.001295 | 0.000947 | 0.002073 | 0.001685 | 0.006325 | 0.022729 | 0.012343 | 0.010635 | 0.034744 | 0.052587 | 0.031915 | 0.030239 | 0.014901 | 0.019770 | 0.003998 | 0.002317 | 0.004757 | 0.009397 | 0.006032 | 0.008577 | 0.046895 | 0.039222 | 0.011426 | 0.004580 | 0.005516 | 0.004525 | 0.006497 | 0.010279 | 0.008630 | 0.004985 | 0.013412 | 0.039766 | 0.181766 |
| min | 1.001017 | 1.004788 | 1.005489 | 1.005814 | 1.014471 | 1.021935 | 1.056934 | 1.105384 | 1.016020 | 1.007096 | 1.001471 | 0.993890 | 0.993313 | 1.001296 | 0.999223 | NaN | 0.999817 | 1.000013 | 0.997681 | 0.995390 | 1.002972 | 1.009822 | 1.005241 | 0.999758 | 1.001350 | 1.000271 | 0.999660 | 0.998656 | 0.998125 | 0.999219 | 0.999405 | 1.001628 | 0.998592 | 1.000520 | 1.001230 | 0.997584 | 0.999135 | 1.000643 | 1.002145 | 0.998591 | 1.001100 | 0.999806 | 0.999738 | 0.995537 | 0.998783 | 0.999160 | 0.999699 | 1.046200 | 1.073849 | 1.042460 |
| 2.5% | 1.002245 | 1.005830 | 1.008270 | 1.007605 | 1.020739 | 1.029307 | 1.071240 | 1.129142 | 1.023062 | 1.009727 | 1.005054 | 1.000093 | 1.004027 | 1.005346 | 1.004812 | NaN | 0.999943 | 1.000731 | 0.998724 | 0.998819 | 1.004176 | 1.012086 | 1.006967 | 1.000747 | 1.002071 | 1.000888 | 0.999731 | 0.999686 | 0.999751 | 1.000618 | 1.000469 | 1.002098 | 1.001309 | 1.001405 | 1.001684 | 0.999992 | 1.002212 | 1.001331 | 1.002502 | 1.001667 | 1.002768 | 1.001088 | 1.000462 | 0.999009 | 1.000119 | 1.000642 | 1.001225 | 1.052142 | 1.084552 | 1.057540 |
| 25% | 1.003977 | 1.008302 | 1.013275 | 1.009732 | 1.025042 | 1.036476 | 1.085945 | 1.168903 | 1.032350 | 1.012968 | 1.007979 | 1.002452 | 1.005543 | 1.008086 | 1.007334 | NaN | 1.000180 | 1.001685 | 1.000601 | 1.001110 | 1.005886 | 1.014926 | 1.008869 | 1.002219 | 1.003771 | 1.002394 | 1.000582 | 1.001020 | 1.001145 | 1.002683 | 1.003374 | 1.005357 | 1.002263 | 1.002866 | 1.003290 | 1.000911 | 1.003975 | 1.004517 | 1.005265 | 1.003245 | 1.005293 | 1.002282 | 1.001192 | 0.999993 | 1.001174 | 1.001934 | 1.002331 | 1.057625 | 1.104250 | 1.130105 |
| 50% | 1.005564 | 1.011347 | 1.017422 | 1.013285 | 1.030792 | 1.046212 | 1.103048 | 1.196672 | 1.037183 | 1.014763 | 1.009234 | 1.003326 | 1.006872 | 1.010248 | 1.009585 | NaN | 1.000432 | 1.002134 | 1.000928 | 1.001974 | 1.006976 | 1.016362 | 1.009864 | 1.003906 | 1.005653 | 1.005236 | 1.002497 | 1.002085 | 1.005214 | 1.004832 | 1.005275 | 1.007934 | 1.002891 | 1.004770 | 1.006027 | 1.002304 | 1.006242 | 1.009081 | 1.006725 | 1.004289 | 1.006597 | 1.002963 | 1.002251 | 1.000538 | 1.002160 | 1.003270 | 1.003223 | 1.066360 | 1.135252 | 1.284267 |
| 75% | 1.006701 | 1.018720 | 1.033033 | 1.021152 | 1.052427 | 1.176875 | 1.159690 | 1.244334 | 1.041997 | 1.018074 | 1.010599 | 1.004768 | 1.009041 | 1.014142 | 1.016854 | NaN | 1.000813 | 1.002690 | 1.001504 | 1.004084 | 1.007973 | 1.018499 | 1.011581 | 1.012797 | 1.010137 | 1.013003 | 1.007155 | 1.008117 | 1.013143 | 1.008851 | 1.010464 | 1.009530 | 1.004776 | 1.007065 | 1.008305 | 1.005438 | 1.010348 | 1.028101 | 1.012841 | 1.005768 | 1.007867 | 1.003978 | 1.003695 | 1.001786 | 1.003426 | 1.005336 | 1.007029 | 1.079461 | 1.178069 | 1.474338 |
| 97.5% | 1.022447 | 1.073989 | 1.159545 | 1.046390 | 1.079502 | 1.269531 | 1.220001 | 1.420512 | 1.059230 | 1.050340 | 1.025488 | 1.017158 | 1.029729 | 1.037032 | 1.045928 | NaN | 1.002368 | 1.006301 | 1.003595 | 1.006475 | 1.010649 | 1.033241 | 1.071368 | 1.034275 | 1.045548 | 1.078651 | 1.084527 | 1.086278 | 1.050446 | 1.057830 | 1.041150 | 1.016551 | 1.008806 | 1.022892 | 1.052507 | 1.028357 | 1.041081 | 1.223036 | 1.197026 | 1.020585 | 1.015497 | 1.014313 | 1.015958 | 1.011869 | 1.023217 | 1.021189 | 1.016518 | 1.098921 | 1.209930 | 1.638419 |
| max | 1.291673 | 1.131301 | 1.253264 | 1.082055 | 1.166288 | 1.338491 | 1.443184 | 2.159810 | 1.138445 | 1.114905 | 1.115892 | 1.059748 | 1.068929 | 1.125101 | 1.347413 | NaN | 1.016929 | 1.016805 | 1.004362 | 1.008139 | 1.011737 | 1.074771 | 1.467675 | 1.092822 | 1.066993 | 1.371152 | 1.466804 | 1.420097 | 1.334974 | 1.126758 | 1.247010 | 1.033603 | 1.016984 | 1.028847 | 1.063301 | 1.053436 | 1.085185 | 1.261031 | 1.216046 | 1.460574 | 1.113820 | 1.110387 | 1.072655 | 1.150027 | 1.266746 | 1.156913 | 1.093964 | 1.143931 | 1.342019 | 2.087167 |
### Rename labels
def labelfy(datum):
out = ''
if '_f' in datum:
out += 'Fixed\n'
else:
out += 'Track\n'
if ('optrev' in datum) and ('_f' in datum):
out += 'Rev. opt.\n'
elif '_f' in datum:
out += 'CF opt.\n'
else:
out += 'Default\n'
if '(curtail)' in datum:
out += 'Curtail'
else:
out += 'Must-run'
return out
columnorder = [
'Fixed\nCF opt.\nCurtail',
'Fixed\nRev. opt.\nMust-run',
'Fixed\nRev. opt.\nCurtail',
'Track\nDefault\nMust-run',
'Track\nDefault\nCurtail',
]
########## Absolute values
iso = 'CAISO'
year = 2017
squeeze = 0.3
ncols = 2
gridspec_kw = {'width_ratios': [2, 2], 'wspace': 0.1}
figsize = (12,4)
dpi = None
###### Column 0: Historical, baseline
datum = 'Revenue'
markets = ['da','rt']
data_baseline = [
### Column 0, vs years
'Revenue_hist_optcf_f(da)(mustrun)',
'Revenue_hist_optcf_f(rt)(mustrun)',
]
### Data-indexed parameters
ylim = [0,145]
ylabel = 'Revenue [$/kWac-yr]'
###### Column 1: single year, TOS
data_tos = [
### Column 1, single year, row 0
'Revenue_dispatched_hist_optcf_f(da)(curtail)',
'Revenue_dispatched_hist_optcf_f(rt)(curtail)',
'Revenue_hist_optrev_f(da)(mustrun)',
'Revenue_hist_optrev_f(rt)(mustrun)',
'Revenue_dispatched_hist_optrev_f(da)(curtail)',
'Revenue_dispatched_hist_optrev_f(rt)(curtail)',
'Revenue_hist_default_t(da)(mustrun)',
'Revenue_hist_default_t(rt)(mustrun)',
'Revenue_hist_default_t(da)(curtail)',
'Revenue_hist_default_t(rt)(curtail)',
]
dfright = pd.melt(
dfplot.loc[(dfplot.ISOwecc==iso)&(dfplot.yearlmp==year),['ISO:Node']+data_tos],
id_vars=['ISO:Node']
)
dfright['market'] = dfright.variable.map(lambda x: 'da' if '(da)' in x else 'rt')
dfright['label'] = dfright.variable.map(labelfy)
dfright['datum'] = dfright.variable.map(lambda x: x.split('_')[0])
### Plot it
plt.close()
f,ax = plt.subplots(1,ncols,sharex='col',sharey='row', gridspec_kw=gridspec_kw,
figsize=figsize, dpi=dpi,
)
### Column 0: Baseline data vs years (fixed, CFopt, must-run)
medians = []
for column in data_baseline:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=column))
pvvm.plots.plotquarthist(
ax=ax[0], dfplot=dfframe, bootstrap=bootstrap, density=True,
hist_range=ylim,
histcolor=(mc['da'] if '(da)' in column else mc['rt']),
direction=('left' if '(da)' in column else 'right'),
squeeze=squeeze,
quartpad=(-0.1 if '(da)' in column else 0.1),
histpad=(-0.15 if '(da)' in column else 0.15),
# medianmarker='_', mediansize=10, medianfacecolor='k'
)
### Column 1: Single-year data vs TOS strategy
for market in markets:
dfframe = dfright.loc[
(dfright.market==market)&(dfright.datum==datum)
].pivot(index='ISO:Node',columns='label',values='value')[columnorder]
medians.append(dfframe[columnorder[-1]].median())
pvvm.plots.plotquarthist(
ax=ax[1], dfplot=dfframe, bootstrap=bootstrap, density=True,
x_locations=range(dfframe.shape[1]),
hist_range=ylim,
histcolor=mc[market],
direction=('left' if market == 'da' else 'right'),
squeeze=squeeze,
quartpad=(-0.06 if market == 'da' else 0.06),
histpad=(-0.11 if market == 'da' else 0.11),
# medianmarker='_', mediansize=10, medianfacecolor='k'
)
### Format axis
### Left
ax[0].set_ylabel(ylabel, weight='bold')
ax[0].set_xlim(2009.4,2018)
ax[0].set_xticks([2010,2012,2014,2016])
ax[0].set_xticklabels(['2010','2012','2014','2016'], rotation=0, ha='center')
ax[0].xaxis.set_minor_locator(AutoMinorLocator(2))
ax[0].set_ylim(0)
### Right
ax[1].set_xlim(-0.6,4.6)
### Grid lines
for i in range(2):
# ax[i].grid(which='major', axis='y', zorder=-1, lw=0.5, ls=(0,(3,6)))
ax[i].grid(which='major', axis='y', zorder=-1, lw=0.25)
### Add title
ax[0].set_title('Baseline: Fixed, CF opt., must-run', weight='bold', size='x-large')
ax[1].set_title('2017', weight='bold', size='x-large')
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[0].legend(
handles=patches, loc='lower left', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7)
plt.show()
for column in data_tos:
print(column)
display(dfplot.groupby(['ISOwecc','yearlmp'])[column].median().unstack('ISOwecc'))
Revenue_dispatched_hist_optcf_f(da)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 91.642956 | NaN | 105.497863 | 77.502471 | 112.758798 | 91.734709 | NaN |
| 2011 | 86.127027 | 163.342554 | 94.790453 | 69.637654 | 102.834552 | 89.623317 | NaN |
| 2012 | 76.242205 | 82.804797 | 75.495416 | 60.435458 | 79.442607 | 70.136512 | NaN |
| 2013 | 107.023902 | 79.332946 | 109.051458 | 61.371157 | 102.792563 | 72.538165 | NaN |
| 2014 | 108.889073 | 88.995977 | 119.572237 | 76.502404 | 113.466331 | 87.085898 | NaN |
| 2015 | 73.412060 | 67.027475 | 81.934850 | 55.225698 | 79.654977 | 67.237333 | 60.160760 |
| 2016 | 59.386822 | 59.710989 | 61.372094 | 55.933880 | 61.697934 | 57.702296 | 50.694685 |
| 2017 | 59.750699 | 58.689548 | 60.681659 | 57.310864 | 60.449408 | 57.116127 | 51.386261 |
Revenue_dispatched_hist_optcf_f(rt)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 94.591012 | NaN | NaN | 74.748461 | 119.518094 | 91.013086 | NaN |
| 2011 | 81.541409 | 147.651843 | 95.315083 | 67.586972 | 102.904595 | 90.658277 | NaN |
| 2012 | 77.769232 | 70.934357 | 75.994444 | 60.223217 | 81.283416 | 71.710441 | NaN |
| 2013 | 100.083051 | 74.627151 | 108.962743 | 60.506492 | 103.764344 | 73.816333 | NaN |
| 2014 | 102.343692 | 81.102552 | 111.813865 | 73.019874 | 108.493946 | 87.620288 | NaN |
| 2015 | 74.092169 | 54.074342 | 77.620474 | 54.325588 | 76.626139 | 64.543321 | 47.904429 |
| 2016 | 59.067273 | 55.466713 | 58.401227 | 55.127363 | 59.708533 | 57.139688 | 43.235054 |
| 2017 | 58.849745 | 55.630056 | 57.996400 | 57.961796 | 57.311387 | 57.362941 | 50.198177 |
Revenue_hist_optrev_f(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 91.780130 | NaN | 105.663158 | 77.839414 | 112.975635 | 92.040608 | NaN |
| 2011 | 86.265822 | 171.663151 | 94.886344 | 69.931324 | 103.184464 | 89.915369 | NaN |
| 2012 | 76.709604 | 84.879363 | 75.649929 | 60.659546 | 79.641184 | 70.261575 | NaN |
| 2013 | 107.137153 | 79.962283 | 109.387344 | 61.384973 | 102.860512 | 72.659026 | NaN |
| 2014 | 109.336825 | 89.566856 | 119.955737 | 76.380780 | 113.653407 | 87.089907 | NaN |
| 2015 | 73.490282 | 68.630289 | 82.175311 | 55.308050 | 79.783423 | 67.245947 | 60.315465 |
| 2016 | 59.958390 | 60.237333 | 61.515509 | 56.104792 | 61.752049 | 57.813635 | 51.169277 |
| 2017 | 61.861851 | 59.110446 | 60.935487 | 57.415140 | 60.635622 | 57.190056 | 51.403162 |
Revenue_hist_optrev_f(rt)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 94.457796 | NaN | NaN | 74.741102 | 117.636972 | 91.053349 | NaN |
| 2011 | 80.960072 | 152.318357 | 95.362220 | 67.565627 | 103.318216 | 90.694282 | NaN |
| 2012 | 77.364859 | 71.723508 | 76.173223 | 60.026348 | 81.424795 | 71.890506 | NaN |
| 2013 | 98.895664 | 75.228430 | 108.980108 | 60.332796 | 104.213755 | 73.925827 | NaN |
| 2014 | 101.134964 | 81.256712 | 111.983595 | 72.876636 | 108.828758 | 87.539732 | NaN |
| 2015 | 71.753935 | 54.244745 | 77.852748 | 54.124228 | 76.857902 | 64.438592 | 45.296315 |
| 2016 | 58.082556 | 55.733125 | 58.163408 | 54.740153 | 59.704050 | 57.255894 | 41.435422 |
| 2017 | 60.152692 | 55.827446 | 58.063913 | 57.723777 | 56.878965 | 57.451400 | 43.670894 |
Revenue_dispatched_hist_optrev_f(da)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 91.789179 | NaN | 105.663158 | 77.839414 | 112.975635 | 92.040608 | NaN |
| 2011 | 86.268224 | 171.673538 | 94.886344 | 69.931324 | 103.184464 | 89.961304 | NaN |
| 2012 | 76.715458 | 84.879363 | 75.649929 | 60.661136 | 79.641184 | 70.261575 | NaN |
| 2013 | 107.137153 | 79.962283 | 109.387344 | 61.384973 | 102.860512 | 72.659026 | NaN |
| 2014 | 109.338862 | 89.597928 | 119.955737 | 76.531748 | 113.653407 | 87.089907 | NaN |
| 2015 | 73.586390 | 68.632155 | 82.175435 | 55.308727 | 79.783423 | 67.245947 | 60.315465 |
| 2016 | 60.015474 | 60.242900 | 61.515509 | 56.104792 | 61.752049 | 57.819155 | 51.169286 |
| 2017 | 62.298697 | 59.110446 | 60.935487 | 57.415140 | 60.635622 | 57.190056 | 52.088554 |
Revenue_dispatched_hist_optrev_f(rt)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 94.766971 | NaN | NaN | 74.868690 | 119.748813 | 91.203533 | NaN |
| 2011 | 81.717005 | 152.839929 | 95.362220 | 67.705604 | 103.378367 | 91.077774 | NaN |
| 2012 | 78.568522 | 71.841250 | 76.173223 | 60.273039 | 81.444175 | 71.916389 | NaN |
| 2013 | 100.368017 | 75.278542 | 108.980108 | 60.526729 | 104.270211 | 73.941487 | NaN |
| 2014 | 103.310672 | 81.294423 | 111.998438 | 73.023562 | 108.864018 | 87.627690 | NaN |
| 2015 | 75.168976 | 54.400999 | 77.988920 | 54.333688 | 76.886210 | 64.577990 | 48.039159 |
| 2016 | 61.502654 | 55.926224 | 58.763805 | 55.277872 | 59.843490 | 57.309070 | 44.276462 |
| 2017 | 64.697122 | 56.012118 | 58.307083 | 58.139875 | 57.422286 | 57.523415 | 54.225021 |
Revenue_hist_default_t(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 109.37760 | NaN | 118.5928 | 90.77040 | 129.87750 | 106.89810 | NaN |
| 2011 | 102.10010 | 193.97660 | 104.4916 | 80.83840 | 114.73255 | 102.60605 | NaN |
| 2012 | 90.92750 | 97.70400 | 83.0162 | 70.27660 | 88.80570 | 80.90280 | NaN |
| 2013 | 126.72775 | 95.37130 | 116.2047 | 70.30390 | 112.78160 | 83.88555 | NaN |
| 2014 | 131.29445 | 107.04620 | 124.8249 | 87.30930 | 119.94860 | 97.96785 | NaN |
| 2015 | 88.43770 | 79.49995 | 88.4539 | 63.84860 | 86.88680 | 76.16690 | 72.9487 |
| 2016 | 73.72300 | 70.06105 | 67.8385 | 64.80445 | 68.98870 | 66.23390 | 63.1331 |
| 2017 | 76.93390 | 69.36600 | 65.3982 | 66.44190 | 68.53490 | 65.49140 | 66.4538 |
Revenue_hist_default_t(rt)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 111.2517 | NaN | NaN | 86.92090 | 132.97770 | 106.03490 | NaN |
| 2011 | 93.1784 | 173.00860 | 105.50010 | 77.71110 | 115.39015 | 104.07990 | NaN |
| 2012 | 92.6374 | 82.76530 | 84.50340 | 69.00330 | 91.31930 | 82.74450 | NaN |
| 2013 | 116.3315 | 89.29950 | 117.86840 | 69.03940 | 116.56040 | 84.98410 | NaN |
| 2014 | 123.2419 | 97.32225 | 116.99075 | 84.53320 | 113.77750 | 97.37860 | NaN |
| 2015 | 86.4133 | 63.83250 | 85.39750 | 62.22110 | 84.56890 | 72.98540 | 54.5495 |
| 2016 | 70.2491 | 64.79050 | 65.02210 | 63.61755 | 67.75440 | 65.74120 | 51.1222 |
| 2017 | 70.3169 | 65.61200 | 62.86090 | 66.82590 | 64.31565 | 65.82685 | 50.9204 |
Revenue_hist_default_t(da)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 109.38250 | NaN | 118.5928 | 90.77040 | 129.87750 | 106.89810 | NaN |
| 2011 | 102.11430 | 194.01830 | 104.4916 | 80.83840 | 114.73255 | 102.60605 | NaN |
| 2012 | 90.93025 | 97.70690 | 83.0162 | 70.27660 | 88.80570 | 80.91480 | NaN |
| 2013 | 126.72775 | 95.37130 | 116.2047 | 70.30390 | 112.78160 | 83.88555 | NaN |
| 2014 | 131.30400 | 107.06850 | 124.8249 | 87.31920 | 119.94860 | 97.96785 | NaN |
| 2015 | 88.48905 | 79.50125 | 88.4555 | 63.95790 | 86.88680 | 76.17050 | 72.9487 |
| 2016 | 73.80240 | 70.08585 | 67.8386 | 64.80445 | 68.98870 | 66.23390 | 63.1331 |
| 2017 | 77.45070 | 69.38910 | 65.3982 | 66.47765 | 68.53490 | 65.49140 | 67.1264 |
Revenue_hist_default_t(rt)(curtail)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
|---|---|---|---|---|---|---|---|
| yearlmp | |||||||
| 2010 | 111.95470 | NaN | NaN | 87.1449 | 135.25100 | 106.37000 | NaN |
| 2011 | 94.63030 | 173.41900 | 105.50010 | 77.9661 | 115.49405 | 104.23975 | NaN |
| 2012 | 94.52365 | 82.94470 | 84.50340 | 69.9133 | 91.36170 | 82.80290 | NaN |
| 2013 | 118.28190 | 89.36690 | 117.86840 | 69.1420 | 116.61170 | 85.04810 | NaN |
| 2014 | 126.15765 | 97.35920 | 116.99685 | 84.8838 | 113.88280 | 97.55180 | NaN |
| 2015 | 90.29275 | 63.96395 | 85.68730 | 62.6773 | 84.87550 | 73.22020 | 57.81550 |
| 2016 | 74.94720 | 64.97755 | 65.75870 | 64.0214 | 67.82570 | 65.78420 | 54.39795 |
| 2017 | 76.77800 | 65.92965 | 63.19395 | 67.0154 | 64.43620 | 65.90585 | 66.26690 |
########## Starting absolute values for track CF-opt
systemtype = 'track'
pricecutoff = 'no'
yearsun = 'tmy'
program = 'PVvalueOptV4'
### Data-indexed parameters
data = [
'OptCF_Azimuth(track)',
'OptRev_Azimuth(track)(da)(mustrun)',
'OptRev_Azimuth(track)(rt)(mustrun)',
]
colindex = [0, 1, 1,]
colindex = dict(zip(data, colindex))
direction = ['right','left','right',]
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [[2011,2017],slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))
### Column-indexed parameters
ylim = [
[90, 270],
[90, 270],
]
xlim = [
[2017, 2018.9],
[2009.4, 2018],
]
majlocs = [45, 45,]
minlocs = [1, 1,]
ylabel = [
'Azimuth',
'Azimuth',
]
note = [
'CF Opt.',
'Revenue Opt. (must-run)',
]
y1 = 1.2 # 1.2 if using note, 1 if no note
y2 = 1.07 # 1.07 if using note, 1.04 if no note
gridspec_kw = {'width_ratios': [0.2, 2,], 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=True, gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]],
density=True, bootstrap=5,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
format_axes=False,
)
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
ax[(row,col)].set_xlim(*xlim[col])
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(-1,-1)].legend(
handles=patches, loc='lower left', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Optimized orientation, 1-axis track', weight='bold', y=y2, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]
print('CAISO 2017')
display(dfplot.loc[(dfplot.ISOwecc=='CAISO')&(dfplot.yearlmp==2017),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
print(datum)
display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
CAISO 2017
| OptCF_Azimuth(track) | OptRev_Azimuth(track)(da)(mustrun) | OptRev_Azimuth(track)(rt)(mustrun) | |
|---|---|---|---|
| count | 2209.000000 | 2209.000000 | 2209.000000 |
| mean | 179.010462 | 174.596272 | 170.409754 |
| std | 1.725166 | 1.544257 | 1.929457 |
| min | 172.581300 | 169.397900 | 163.715300 |
| 2.5% | 175.767100 | 171.660620 | 166.723000 |
| 25% | 177.990600 | 173.524600 | 169.114200 |
| 50% | 178.988600 | 174.628600 | 170.378100 |
| 75% | 180.043100 | 175.732000 | 171.763000 |
| 97.5% | 182.775400 | 177.459500 | 174.119160 |
| max | 186.961600 | 179.733500 | 176.149900 |
median
| OptCF_Azimuth(track) | OptRev_Azimuth(track)(da)(mustrun) | OptRev_Azimuth(track)(rt)(mustrun) | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | |||||||||||||||||||||
| 2010 | 178.97240 | NaN | 185.05600 | 179.54370 | 181.1328 | 180.41720 | NaN | 178.58595 | NaN | 186.91200 | 183.16280 | 182.34860 | 182.21985 | NaN | 179.13560 | NaN | NaN | 183.29540 | 181.42395 | 183.09640 | NaN |
| 2011 | 178.97240 | 178.3847 | 184.96160 | 179.57210 | 181.1328 | 180.41390 | NaN | 178.83910 | 169.6138 | 187.68390 | 182.52890 | 181.41745 | 181.61540 | NaN | 179.34280 | 174.99495 | 189.56430 | 183.44170 | 181.99635 | 182.24200 | NaN |
| 2012 | 178.97240 | 178.3822 | 184.94860 | 179.60610 | 181.1328 | 180.34830 | NaN | 175.68760 | 173.1899 | 187.58490 | 180.40680 | 181.02490 | 180.94900 | NaN | 179.31285 | 174.91210 | 187.28660 | 181.27530 | 181.26600 | 181.82320 | NaN |
| 2013 | 178.97240 | 178.3822 | 184.87650 | 179.63755 | 181.1328 | 180.34185 | NaN | 178.96795 | 177.4774 | 196.27420 | 182.19165 | 184.39430 | 181.25070 | NaN | 180.64480 | 175.85250 | 197.92920 | 183.40790 | 184.72880 | 182.75880 | NaN |
| 2014 | 178.98235 | 178.3769 | 184.86325 | 179.20680 | 181.1328 | 180.31040 | NaN | 177.45800 | 177.6657 | 263.67295 | 181.44635 | 190.26280 | 186.44160 | NaN | 176.21910 | 179.44475 | 263.56410 | 183.20460 | 261.83265 | 190.60390 | NaN |
| 2015 | 178.98235 | 178.3858 | 184.97710 | 179.20680 | 181.1328 | 180.31020 | 181.61460 | 177.12110 | 173.7177 | 193.04880 | 178.29650 | 184.35460 | 182.45870 | 179.4361 | 177.56240 | 178.49825 | 187.58875 | 179.60560 | 181.22120 | 183.89120 | 183.4163 |
| 2016 | 178.98400 | 178.3858 | 185.08020 | 179.21950 | 181.1328 | 180.28070 | 181.97755 | 175.74770 | 173.6436 | 187.69580 | 176.85090 | 182.54630 | 180.33620 | 178.8080 | 169.46480 | 174.21235 | 185.44820 | 177.81440 | 183.79270 | 181.15270 | 173.5283 |
| 2017 | 178.98860 | 178.3869 | 185.09420 | 179.21950 | 181.1328 | 180.27610 | 181.15130 | 174.62860 | 175.4276 | 189.65600 | 177.52110 | 182.86015 | 180.85790 | 178.9640 | 170.37810 | 176.01455 | 188.01640 | 178.61555 | 182.55705 | 179.44275 | 175.4903 |
max
| OptCF_Azimuth(track) | OptRev_Azimuth(track)(da)(mustrun) | OptRev_Azimuth(track)(rt)(mustrun) | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | |||||||||||||||||||||
| 2010 | 186.9616 | NaN | 191.2858 | 184.8219 | 185.4557 | 185.7092 | NaN | 187.6270 | NaN | 194.2195 | 187.5372 | 187.3184 | 188.8358 | NaN | 265.3918 | NaN | NaN | 189.8596 | 186.8429 | 196.5247 | NaN |
| 2011 | 186.9616 | 181.269 | 191.2858 | 184.8219 | 185.4557 | 185.7092 | NaN | 187.9552 | 176.9438 | 194.6961 | 185.6642 | 187.9583 | 186.4591 | NaN | 189.8977 | 182.1243 | 198.0822 | 187.2886 | 185.9526 | 186.9667 | NaN |
| 2012 | 186.9616 | 181.269 | 191.2858 | 184.8219 | 185.4557 | 185.7092 | NaN | 186.5741 | 181.2621 | 195.1537 | 184.3992 | 186.5211 | 187.9490 | NaN | 198.5139 | 183.2112 | 195.8348 | 188.5268 | 188.1196 | 188.5675 | NaN |
| 2013 | 186.9616 | 181.269 | 191.2858 | 184.8219 | 185.4557 | 185.7092 | NaN | 185.8293 | 179.5786 | 268.6562 | 187.1347 | 195.8164 | 188.0083 | NaN | 190.4561 | 184.2788 | 266.8571 | 191.5648 | 192.6819 | 195.7096 | NaN |
| 2014 | 186.9616 | 181.269 | 191.2858 | 184.8219 | 185.4557 | 185.7092 | NaN | 184.7099 | 182.9918 | 269.7753 | 191.0909 | 269.4239 | 268.5646 | NaN | 184.2766 | 189.8853 | 269.8379 | 191.9578 | 269.5888 | 264.1737 | NaN |
| 2015 | 186.9616 | 181.269 | 191.2858 | 184.8219 | 185.4557 | 185.7092 | 188.1054 | 182.6962 | 179.2040 | 269.6479 | 185.8616 | 193.8626 | 191.5178 | 184.9359 | 184.8021 | 194.2804 | 268.3690 | 188.4462 | 194.4349 | 202.9547 | 190.7999 |
| 2016 | 186.9616 | 181.269 | 191.2858 | 184.8219 | 185.4557 | 185.7092 | 188.1054 | 182.0394 | 179.1025 | 196.2937 | 183.0558 | 188.5322 | 185.9884 | 184.5777 | 180.6193 | 182.1753 | 196.3411 | 189.9954 | 192.3094 | 191.2423 | 193.9057 |
| 2017 | 186.9616 | 181.269 | 191.2858 | 184.8219 | 185.4557 | 185.7092 | 191.9505 | 179.7335 | 180.5620 | 262.4102 | 181.9695 | 190.6527 | 188.3331 | 184.3893 | 176.1499 | 190.8237 | 267.5094 | 184.1256 | 194.3232 | 203.0064 | 190.7729 |
OptCF_Azimuth(track)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.00000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 178.989961 | 178.983531 | 178.987637 | 178.998159 | 179.007179 | 179.007143 | 179.00766 | 179.010462 | 178.290023 | 178.287402 | 178.288748 | 178.286875 | 178.293928 | 178.293972 | 178.295346 | 184.756251 | 184.703406 | 184.690696 | 184.649794 | 184.648735 | 184.783086 | 184.858498 | 184.932275 | 179.055477 | 179.030391 | 179.041531 | 179.067105 | 179.046885 | 179.053782 | 179.080310 | 179.086934 | 180.994657 | 180.969683 | 181.002653 | 180.988701 | 180.976689 | 180.973595 | 180.967991 | 180.967991 | 180.429168 | 180.423942 | 180.344297 | 180.346795 | 180.321169 | 180.320242 | 180.299894 | 180.291709 | 181.234842 | 181.609208 | 180.996075 |
| std | 1.738383 | 1.747644 | 1.739322 | 1.729052 | 1.727049 | 1.726302 | 1.72609 | 1.725166 | 1.045779 | 1.046590 | 1.046597 | 1.045123 | 1.045572 | 1.045501 | 1.046537 | 2.245942 | 2.286690 | 2.233978 | 2.247046 | 2.237022 | 2.192585 | 2.166196 | 2.122314 | 2.577188 | 2.635250 | 2.650976 | 2.641757 | 2.079048 | 2.079573 | 2.047661 | 2.023391 | 1.696755 | 1.701677 | 1.700095 | 1.701083 | 1.703607 | 1.702866 | 1.704928 | 1.704928 | 1.978276 | 1.974949 | 2.024270 | 2.025007 | 2.010171 | 2.011730 | 2.015444 | 2.009901 | 2.571735 | 2.141247 | 2.541189 |
| min | 172.581300 | 172.581300 | 172.581300 | 172.581300 | 172.581300 | 172.581300 | 172.58130 | 172.581300 | 173.835700 | 173.835700 | 173.835700 | 173.835700 | 173.835700 | 173.835700 | 173.835700 | 178.294800 | 178.294800 | 178.294800 | 178.038400 | 178.038400 | 177.048300 | 177.048300 | 177.048300 | 169.918900 | 169.918900 | 169.918900 | 169.918900 | 169.918900 | 169.918900 | 169.918900 | 169.918900 | 175.790500 | 175.790500 | 175.790500 | 175.790500 | 175.790500 | 175.790500 | 175.790500 | 175.790500 | 172.703200 | 172.703200 | 172.703200 | 172.703200 | 172.703200 | 172.703200 | 172.703200 | 172.703200 | 171.399300 | 171.399300 | 168.207600 |
| 2.5% | 175.733953 | 175.712400 | 175.712400 | 175.736705 | 175.746755 | 175.747425 | 175.74776 | 175.767100 | 176.205380 | 176.207345 | 176.207435 | 176.207480 | 176.207503 | 176.207503 | 176.207345 | 179.321800 | 179.017500 | 179.017500 | 179.203980 | 179.081603 | 179.594075 | 179.708180 | 179.727600 | 173.286115 | 172.092500 | 172.092500 | 172.092500 | 173.733010 | 173.739270 | 173.789350 | 173.836300 | 177.344980 | 177.354093 | 177.365102 | 177.370607 | 177.374277 | 177.375195 | 177.376113 | 177.376113 | 176.580080 | 176.608325 | 176.322355 | 176.306805 | 176.306400 | 176.308560 | 176.306400 | 176.306400 | 174.192000 | 175.383065 | 173.140000 |
| 25% | 177.972600 | 177.972600 | 177.973900 | 177.988300 | 177.990600 | 177.990600 | 177.99060 | 177.990600 | 177.552900 | 177.552250 | 177.553700 | 177.555000 | 177.558900 | 177.558900 | 177.558900 | 183.558300 | 183.449300 | 183.449450 | 183.439900 | 183.487250 | 183.573500 | 183.648100 | 183.834600 | 177.465600 | 177.434600 | 177.434600 | 177.481100 | 178.185100 | 178.185100 | 178.197875 | 178.185100 | 180.065300 | 180.064375 | 180.065300 | 180.065300 | 180.062525 | 180.030800 | 180.000000 | 180.000000 | 179.011650 | 179.009100 | 178.895850 | 178.905200 | 178.902750 | 178.894300 | 178.881700 | 178.878650 | 180.271700 | 180.818000 | 180.533800 |
| 50% | 178.972400 | 178.972400 | 178.972400 | 178.972400 | 178.982350 | 178.982350 | 178.98400 | 178.988600 | 178.384700 | 178.382200 | 178.382200 | 178.376900 | 178.385800 | 178.385800 | 178.386900 | 185.056000 | 184.961600 | 184.948600 | 184.876500 | 184.863250 | 184.977100 | 185.080200 | 185.094200 | 179.543700 | 179.572100 | 179.606100 | 179.637550 | 179.206800 | 179.206800 | 179.219500 | 179.219500 | 181.132800 | 181.132800 | 181.132800 | 181.132800 | 181.132800 | 181.132800 | 181.132800 | 181.132800 | 180.417200 | 180.413900 | 180.348300 | 180.341850 | 180.310400 | 180.310200 | 180.280700 | 180.276100 | 181.614600 | 181.977550 | 181.151300 |
| 75% | 179.989300 | 179.987800 | 180.001725 | 180.026525 | 180.031000 | 180.031000 | 180.03100 | 180.043100 | 178.962300 | 178.961900 | 178.962300 | 178.961800 | 178.962450 | 178.962450 | 178.964600 | 186.385600 | 186.342100 | 186.252050 | 186.231000 | 186.232525 | 186.339525 | 186.407300 | 186.468600 | 180.617000 | 180.648600 | 180.648600 | 180.680525 | 180.411200 | 180.430600 | 180.379075 | 180.379075 | 181.606200 | 181.606200 | 181.818700 | 181.806975 | 181.806975 | 181.801650 | 181.796325 | 181.796325 | 181.891725 | 181.885550 | 181.820450 | 181.825000 | 181.780025 | 181.780000 | 181.757000 | 181.754950 | 182.842200 | 182.787200 | 182.355700 |
| 97.5% | 182.775400 | 182.775400 | 182.775400 | 182.775400 | 182.775400 | 182.775400 | 182.77540 | 182.775400 | 180.338625 | 180.337300 | 180.359825 | 180.358500 | 180.357837 | 180.357837 | 180.362475 | 188.208320 | 188.204890 | 188.164665 | 188.204400 | 188.203933 | 188.206237 | 188.205870 | 188.204187 | 183.769915 | 183.614143 | 183.554230 | 183.518282 | 182.243185 | 182.237177 | 182.158737 | 182.197957 | 183.939800 | 183.939800 | 183.939800 | 183.939800 | 183.939800 | 183.939800 | 183.939800 | 183.939800 | 184.004700 | 184.004700 | 183.961185 | 183.956900 | 183.956900 | 183.956900 | 183.960450 | 183.953300 | 185.032980 | 184.966882 | 184.920900 |
| max | 186.961600 | 186.961600 | 186.961600 | 186.961600 | 186.961600 | 186.961600 | 186.96160 | 186.961600 | 181.269000 | 181.269000 | 181.269000 | 181.269000 | 181.269000 | 181.269000 | 181.269000 | 191.285800 | 191.285800 | 191.285800 | 191.285800 | 191.285800 | 191.285800 | 191.285800 | 191.285800 | 184.821900 | 184.821900 | 184.821900 | 184.821900 | 184.821900 | 184.821900 | 184.821900 | 184.821900 | 185.455700 | 185.455700 | 185.455700 | 185.455700 | 185.455700 | 185.455700 | 185.455700 | 185.455700 | 185.709200 | 185.709200 | 185.709200 | 185.709200 | 185.709200 | 185.709200 | 185.709200 | 185.709200 | 188.105400 | 188.105400 | 191.950500 |
OptRev_Azimuth(track)(da)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 178.713882 | 178.999812 | 175.495930 | 179.086554 | 177.576333 | 177.125808 | 175.782676 | 174.596272 | 169.861246 | 173.260760 | 177.362059 | 177.545527 | 173.838147 | 173.639293 | 175.331841 | 186.712780 | 187.473796 | 187.350811 | 216.386349 | 248.494249 | 195.225530 | 187.568029 | 189.867133 | 182.819345 | 182.112153 | 180.058041 | 181.940659 | 182.245056 | 178.841647 | 177.250035 | 177.735048 | 181.748337 | 181.697707 | 180.835985 | 183.688099 | 213.574147 | 183.623374 | 181.179863 | 182.001034 | 182.145177 | 181.663434 | 181.020327 | 181.241848 | 188.216710 | 182.392181 | 180.280785 | 180.872295 | 179.128759 | 178.312119 | 177.475891 |
| std | 1.864467 | 1.625216 | 2.310853 | 1.516823 | 1.455543 | 1.348115 | 1.513219 | 1.544257 | 1.851081 | 1.379359 | 1.055502 | 1.420970 | 1.029631 | 1.152666 | 1.362900 | 2.455959 | 2.331583 | 2.610775 | 33.350727 | 29.007532 | 13.556203 | 2.715838 | 6.143691 | 2.034358 | 1.799215 | 2.102332 | 2.205400 | 3.028569 | 2.332256 | 2.089512 | 1.827901 | 2.291019 | 1.924960 | 2.363182 | 2.890059 | 41.491710 | 4.494629 | 3.545547 | 2.379871 | 1.919041 | 1.689754 | 1.937177 | 1.966693 | 12.142939 | 2.511179 | 2.151055 | 2.293359 | 2.134882 | 2.187964 | 3.205738 |
| min | 172.377200 | 173.537100 | 167.257900 | 173.479600 | 172.471500 | 172.581300 | 170.985500 | 169.397900 | 166.111300 | 162.030200 | 169.723400 | 166.797500 | 170.108600 | 166.290700 | 162.741100 | 179.234900 | 180.594800 | 179.244600 | 178.922300 | 185.571400 | 174.972400 | 177.595300 | 177.231600 | 174.112100 | 174.379600 | 172.315200 | 172.734200 | 174.152200 | 171.479300 | 169.767200 | 169.731600 | 175.729400 | 177.312800 | 175.832200 | 176.321400 | 90.492300 | 162.131700 | 171.974900 | 175.974500 | 174.588300 | 175.820000 | 174.910600 | 174.909200 | 91.099800 | 174.450600 | 172.982500 | 173.475400 | 171.047900 | 167.008500 | 165.970900 |
| 2.5% | 175.452193 | 176.070600 | 170.528108 | 176.401350 | 174.989812 | 174.562100 | 172.686900 | 171.660620 | 166.877345 | 171.218640 | 174.491000 | 175.078100 | 171.982600 | 171.486870 | 172.756500 | 181.086060 | 181.577020 | 181.054250 | 185.560600 | 192.989075 | 184.275700 | 180.528400 | 181.998712 | 178.804580 | 177.190517 | 174.183950 | 175.150390 | 177.999190 | 174.997825 | 174.207437 | 174.396980 | 177.154100 | 177.552400 | 176.607788 | 178.308657 | 176.671400 | 174.117000 | 172.462187 | 177.046525 | 178.085567 | 178.391087 | 177.170330 | 177.373933 | 179.768450 | 177.404480 | 175.835950 | 176.161300 | 173.417480 | 172.550465 | 169.475750 |
| 25% | 177.615900 | 177.934100 | 174.019500 | 178.123000 | 176.585425 | 176.224950 | 174.849100 | 173.524600 | 168.498650 | 172.572550 | 176.882800 | 176.915900 | 173.205625 | 172.964100 | 174.528800 | 185.330100 | 186.373100 | 185.937800 | 192.758200 | 259.918325 | 190.492950 | 186.349500 | 187.501900 | 181.812050 | 181.070575 | 178.870800 | 180.989400 | 179.978300 | 177.168150 | 175.700975 | 176.502800 | 179.916500 | 180.273125 | 178.949950 | 181.739400 | 184.401600 | 181.802500 | 179.174975 | 180.498175 | 180.881675 | 180.489825 | 179.719100 | 179.958800 | 184.145700 | 180.686200 | 178.832700 | 179.353075 | 178.349200 | 177.362675 | 176.004600 |
| 50% | 178.585950 | 178.839100 | 175.687600 | 178.967950 | 177.458000 | 177.121100 | 175.747700 | 174.628600 | 169.613800 | 173.189900 | 177.477400 | 177.665700 | 173.717700 | 173.643600 | 175.427600 | 186.912000 | 187.683900 | 187.584900 | 196.274200 | 263.672950 | 193.048800 | 187.695800 | 189.656000 | 183.162800 | 182.528900 | 180.406800 | 182.191650 | 181.446350 | 178.296500 | 176.850900 | 177.521100 | 182.348600 | 181.417450 | 181.024900 | 184.394300 | 190.262800 | 184.354600 | 182.546300 | 182.860150 | 182.219850 | 181.615400 | 180.949000 | 181.250700 | 186.441600 | 182.458700 | 180.336200 | 180.857900 | 179.436100 | 178.808000 | 178.964000 |
| 75% | 179.845400 | 179.974300 | 177.087375 | 180.013300 | 178.463200 | 178.077500 | 176.727400 | 175.732000 | 171.213150 | 173.952700 | 178.004950 | 178.337700 | 174.379200 | 174.372150 | 176.179300 | 188.481200 | 189.040000 | 189.107350 | 263.185500 | 266.194550 | 195.922800 | 189.523000 | 191.105600 | 184.014750 | 183.336025 | 181.515700 | 183.189775 | 184.435875 | 180.788000 | 178.722200 | 179.203775 | 183.578500 | 183.258625 | 183.213100 | 185.124600 | 266.104900 | 186.592050 | 184.004100 | 183.448200 | 183.556700 | 182.852050 | 182.406750 | 182.500000 | 188.915800 | 183.970100 | 181.866800 | 182.557425 | 180.451300 | 179.553150 | 179.594400 |
| 97.5% | 182.828800 | 182.244900 | 179.699373 | 182.370370 | 180.814328 | 179.761250 | 178.945280 | 177.459500 | 173.704110 | 175.837650 | 179.058010 | 179.727220 | 176.307325 | 175.741400 | 177.557005 | 190.927400 | 191.177080 | 191.838900 | 268.198500 | 268.159900 | 260.192500 | 191.683200 | 197.897250 | 185.934585 | 184.941475 | 183.834160 | 185.529800 | 188.241977 | 183.396160 | 181.000687 | 181.189900 | 185.721400 | 185.395627 | 184.621495 | 189.417835 | 267.917123 | 189.426795 | 185.131962 | 185.960913 | 185.541300 | 184.808300 | 184.439740 | 185.261342 | 201.673300 | 187.314660 | 184.245950 | 185.107700 | 182.508560 | 181.782800 | 181.743100 |
| max | 187.627000 | 187.955200 | 186.574100 | 185.829300 | 184.709900 | 182.696200 | 182.039400 | 179.733500 | 176.943800 | 181.262100 | 179.578600 | 182.991800 | 179.204000 | 179.102500 | 180.562000 | 194.219500 | 194.696100 | 195.153700 | 268.656200 | 269.775300 | 269.647900 | 196.293700 | 262.410200 | 187.537200 | 185.664200 | 184.399200 | 187.134700 | 191.090900 | 185.861600 | 183.055800 | 181.969500 | 187.318400 | 187.958300 | 186.521100 | 195.816400 | 269.423900 | 193.862600 | 188.532200 | 190.652700 | 188.835800 | 186.459100 | 187.949000 | 188.008300 | 268.564600 | 191.517800 | 185.988400 | 188.333100 | 184.935900 | 184.577700 | 184.389300 |
OptRev_Azimuth(track)(rt)(mustrun)
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 178.987086 | 179.472997 | 178.357221 | 180.696715 | 176.211363 | 177.466401 | 169.555050 | 170.409754 | 174.842097 | 174.804391 | 175.836472 | 179.446023 | 178.357593 | 174.047470 | 175.855528 | NaN | 189.569873 | 186.940394 | 204.275620 | 252.154475 | 188.184978 | 185.098017 | 187.865734 | 183.397075 | 183.211134 | 181.244416 | 183.454520 | 183.765346 | 179.696149 | 178.270592 | 178.538832 | 180.913046 | 181.808407 | 181.266061 | 184.044756 | 227.073927 | 180.026597 | 181.216105 | 181.502560 | 182.836921 | 182.224260 | 181.891185 | 182.269944 | 191.619911 | 183.987359 | 181.024038 | 179.342517 | 182.944008 | 174.477946 | 175.473331 |
| std | 4.698432 | 2.462558 | 5.345981 | 2.148960 | 1.664582 | 1.822996 | 2.964351 | 1.929457 | 2.738249 | 2.329700 | 1.605088 | 2.299832 | 1.905888 | 1.884440 | 2.147298 | NaN | 2.365967 | 2.748492 | 20.153979 | 25.958367 | 6.686715 | 3.245155 | 16.523640 | 2.216181 | 1.817621 | 2.378619 | 2.667391 | 2.982294 | 3.492462 | 2.939310 | 2.576068 | 2.474732 | 2.314364 | 2.150944 | 3.020389 | 42.139482 | 6.018570 | 6.907976 | 3.683422 | 2.229697 | 1.735962 | 2.240095 | 3.515749 | 8.648737 | 2.982964 | 2.359273 | 2.998628 | 2.743780 | 3.816782 | 2.726003 |
| min | 169.161100 | 171.600900 | 92.801100 | 174.122000 | 171.409400 | 169.861700 | 160.746700 | 163.715300 | 168.961600 | 152.150500 | 168.547600 | 161.409200 | 159.097500 | 155.914900 | 159.061300 | NaN | 183.835900 | 177.801200 | 181.957000 | 186.607800 | 170.906700 | 174.901700 | 90.519700 | 176.280400 | 177.458000 | 172.754000 | 172.408900 | 178.792900 | 169.383900 | 169.659200 | 167.140200 | 173.928400 | 175.620800 | 176.520100 | 176.705500 | 90.013200 | 158.546100 | 149.125300 | 167.626600 | 172.959100 | 175.977500 | 174.397600 | 166.842000 | 176.491000 | 165.357100 | 173.821000 | 167.169200 | 172.733600 | 162.674100 | 164.529000 |
| 2.5% | 173.063925 | 174.261500 | 170.884233 | 176.856070 | 172.986885 | 173.622587 | 163.372390 | 166.723000 | 170.043588 | 170.575715 | 172.420375 | 174.994798 | 174.469310 | 170.452220 | 171.922375 | NaN | 184.790220 | 180.427090 | 187.736200 | 193.363288 | 179.178237 | 177.995400 | 175.949500 | 178.147870 | 179.493943 | 175.694660 | 176.035585 | 179.351195 | 172.967233 | 173.837175 | 173.216455 | 176.234488 | 177.442500 | 177.042597 | 177.864600 | 176.671400 | 166.960200 | 154.471600 | 170.885600 | 177.932797 | 178.898650 | 177.376175 | 170.500018 | 180.691425 | 178.335100 | 176.053050 | 173.427513 | 176.388260 | 168.326085 | 169.993200 |
| 25% | 176.974500 | 178.058600 | 175.448300 | 179.056100 | 174.951975 | 176.301350 | 167.800900 | 169.114200 | 172.501150 | 173.928400 | 175.070850 | 178.530325 | 177.619925 | 173.357525 | 174.732975 | NaN | 188.008500 | 185.422200 | 195.529700 | 260.995000 | 184.963900 | 183.343500 | 185.847800 | 182.159500 | 181.990125 | 180.128700 | 182.198100 | 181.458800 | 176.825625 | 176.123700 | 177.201450 | 179.027900 | 180.461275 | 179.759800 | 182.225900 | 187.296800 | 178.094000 | 179.002275 | 179.434675 | 181.496100 | 181.005700 | 180.413950 | 181.073525 | 186.497800 | 182.136200 | 179.561600 | 177.317275 | 181.897300 | 171.742850 | 174.247300 |
| 50% | 179.135600 | 179.342800 | 179.312850 | 180.644800 | 176.219100 | 177.562400 | 169.464800 | 170.378100 | 174.994950 | 174.912100 | 175.852500 | 179.444750 | 178.498250 | 174.212350 | 176.014550 | NaN | 189.564300 | 187.286600 | 197.929200 | 263.564100 | 187.588750 | 185.448200 | 188.016400 | 183.295400 | 183.441700 | 181.275300 | 183.407900 | 183.204600 | 179.605600 | 177.814400 | 178.615550 | 181.423950 | 181.996350 | 181.266000 | 184.728800 | 261.832650 | 181.221200 | 183.792700 | 182.557050 | 183.096400 | 182.242000 | 181.823200 | 182.758800 | 190.603900 | 183.891200 | 181.152700 | 179.442750 | 183.416300 | 173.528300 | 175.490300 |
| 75% | 180.642800 | 180.759100 | 180.927375 | 182.156300 | 177.229900 | 178.809200 | 171.611800 | 171.763000 | 176.961225 | 175.945900 | 176.608950 | 180.440100 | 179.284725 | 174.989625 | 176.992925 | NaN | 191.276400 | 188.572400 | 201.531000 | 265.631250 | 190.710600 | 187.233000 | 189.341700 | 184.671200 | 184.509100 | 182.742300 | 185.000350 | 185.947425 | 182.525100 | 180.611350 | 180.596350 | 182.510700 | 183.428900 | 182.704800 | 185.909300 | 266.676200 | 184.295100 | 184.383000 | 183.888000 | 184.315875 | 183.498000 | 183.490750 | 184.152500 | 195.085150 | 185.772700 | 182.613800 | 181.416100 | 184.766300 | 176.552775 | 176.907200 |
| 97.5% | 184.336480 | 184.788200 | 185.862000 | 185.015190 | 179.495805 | 180.614562 | 175.403470 | 174.119160 | 179.601650 | 178.446550 | 178.422840 | 183.716845 | 181.101075 | 176.917755 | 179.260375 | NaN | 193.672570 | 191.327160 | 263.644800 | 268.196653 | 196.810300 | 190.692140 | 201.980138 | 187.684730 | 186.225755 | 185.510260 | 189.242070 | 190.024285 | 185.570640 | 183.832825 | 183.067072 | 185.546992 | 185.117100 | 184.591700 | 189.135643 | 267.712900 | 189.264345 | 189.341000 | 186.116162 | 186.514900 | 185.422100 | 186.157835 | 187.473073 | 205.996887 | 190.315000 | 185.373650 | 184.163900 | 187.358260 | 183.940695 | 181.248000 |
| max | 265.391800 | 189.897700 | 198.513900 | 190.456100 | 184.276600 | 184.802100 | 180.619300 | 176.149900 | 182.124300 | 183.211200 | 184.278800 | 189.885300 | 194.280400 | 182.175300 | 190.823700 | NaN | 198.082200 | 195.834800 | 266.857100 | 269.837900 | 268.369000 | 196.341100 | 267.509400 | 189.859600 | 187.288600 | 188.526800 | 191.564800 | 191.957800 | 188.446200 | 189.995400 | 184.125600 | 186.842900 | 185.952600 | 188.119600 | 192.681900 | 269.588800 | 194.434900 | 192.309400 | 194.323200 | 196.524700 | 186.966700 | 188.567500 | 195.709600 | 264.173700 | 202.954700 | 191.242300 | 203.006400 | 190.799900 | 193.905700 | 190.772900 |
########## Starting absolute values for track CF-opt
### Data-indexed parameters
data = [
'CF_track,optrev,da,mustrun/CF_track,default,da,mustrun',
'CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun',
'Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun',
'Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun',
]
colindex = [0, 0, 1, 1,]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))
### Column-indexed parameters
ylim = [
[0.90, 1.04],
[0.96,1.10],
]
xlim = [
[2009.4, 2018],
[2009.4, 2018],
]
majlocs = [0.05,0.05,0.05,0.05,]
minlocs = [2,2,2,2,]
ylabel = [
'Capacity Factor',
'Revenue',
]
note = [
'(must-run)',
'(must-run)',
# '(curtailable)',
# '(curtailable)',
]
y1 = 1.2 # 1.2 if using note, 1 if no note
y2 = 1.07 # 1.07 if using note, 1.04 if no note
gridspec_kw = {'width_ratios': [2, 2,]}#, 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey=False, gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]],
density=True, bootstrap=bootstrap,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
format_axes=False,
)
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
ax[(row,col)].set_xlim(*xlim[col])
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(0,-1)].legend(
handles=patches, loc='upper right', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Ratio, Revenue-opt. vs. default, 1-axis track, must-run',
weight='bold', y=y2, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]
print('ISONE 2014')
display(dfplot.loc[(dfplot.ISOwecc=='ISONE')&(dfplot.yearlmp==2014),data].describe(percentiles=fractions))
print('median')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].median().unstack('ISOwecc'))
print('max')
display(dfplot.groupby(['ISOwecc','yearlmp'])[data].max().unstack('ISOwecc'))
for datum in data:
print(datum)
display(dfplot.groupby(['ISOwecc','yearlmp'])[datum].describe(percentiles=fractions).T)
ISONE 2014
| CF_track,optrev,da,mustrun/CF_track,default,da,mustrun | CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun | Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun | Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun | |
|---|---|---|---|---|
| count | 612.000000 | 612.000000 | 612.000000 | 612.000000 |
| mean | 0.950009 | 0.946429 | 1.006034 | 1.002358 |
| std | 0.026693 | 0.024231 | 0.007055 | 0.007336 |
| min | 0.918200 | 0.919893 | 0.974291 | 0.969552 |
| 2.5% | 0.926324 | 0.926118 | 0.993690 | 0.987178 |
| 25% | 0.932030 | 0.931881 | 1.002899 | 0.998801 |
| 50% | 0.935889 | 0.935391 | 1.005401 | 1.003508 |
| 75% | 0.952117 | 0.946606 | 1.008337 | 1.005644 |
| 97.5% | 0.998058 | 0.997715 | 1.023781 | 1.016771 |
| max | 0.999892 | 0.999710 | 1.028671 | 1.023783 |
median
| CF_track,optrev,da,mustrun/CF_track,default,da,mustrun | CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun | Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun | Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun | |||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | ||||||||||||||||||||||||||||
| 2010 | 0.999985 | NaN | 0.999817 | 0.999743 | 1.000120 | 0.999957 | NaN | 0.999930 | NaN | NaN | 0.999645 | 1.000018 | 0.999884 | NaN | 1.000017 | NaN | 1.000232 | 1.000038 | 1.000101 | 1.000011 | NaN | 1.000006 | NaN | NaN | 1.000060 | 1.000019 | 1.000014 | NaN |
| 2011 | 1.000033 | 0.998260 | 0.999637 | 0.999896 | 1.000037 | 1.000037 | NaN | 0.999993 | 0.999827 | 0.999259 | 0.999819 | 1.000029 | 1.000008 | NaN | 1.000026 | 1.003243 | 1.000502 | 1.000039 | 1.000075 | 1.000030 | NaN | 1.000024 | 1.001344 | 1.000912 | 1.000052 | 1.000129 | 1.000005 | NaN |
| 2012 | 0.999685 | 0.999595 | 0.999169 | 1.000027 | 1.000018 | 0.999965 | NaN | 0.999886 | 0.999874 | 0.999282 | 0.999987 | 1.000080 | 0.999931 | NaN | 1.000277 | 1.001375 | 0.999896 | 1.000008 | 1.000069 | 1.000002 | NaN | 1.000042 | 1.001156 | 0.999907 | 1.000031 | 1.000046 | 0.999999 | NaN |
| 2013 | 1.000026 | 1.000004 | 0.997261 | 0.999882 | 0.999821 | 0.999979 | NaN | 0.999967 | 0.999841 | 0.996403 | 0.999726 | 0.999714 | 0.999879 | NaN | 1.000001 | 1.000107 | 1.002520 | 1.000059 | 1.000127 | 1.000009 | NaN | 1.000013 | 1.000766 | 1.004215 | 1.000103 | 0.999984 | 1.000041 | NaN |
| 2014 | 0.999940 | 1.000019 | 0.935889 | 0.999876 | 0.998162 | 0.999359 | NaN | 0.999779 | 0.999990 | 0.935391 | 0.999751 | 0.931320 | 0.998010 | NaN | 1.000070 | 1.000191 | 1.005401 | 1.000050 | 1.000367 | 1.001401 | NaN | 1.000377 | 1.000007 | 1.003508 | 1.000307 | 0.999673 | 1.004511 | NaN |
| 2015 | 0.999927 | 0.999478 | 0.996990 | 1.000003 | 0.999513 | 0.999850 | 0.999989 | 0.999944 | 1.000007 | 0.998985 | 0.999854 | 0.999666 | 0.999671 | 1.000085 | 1.000084 | 1.001089 | 0.999301 | 1.000033 | 0.999705 | 1.000088 | 1.000004 | 1.000120 | 1.000103 | 0.999214 | 1.000117 | 1.000067 | 1.000146 | 1.000271 |
| 2016 | 0.999552 | 0.999408 | 0.999358 | 0.999861 | 0.999744 | 0.999960 | 0.999864 | 0.997386 | 0.999543 | 0.999770 | 0.999854 | 0.999680 | 0.999920 | 0.998704 | 1.000000 | 1.000978 | 1.000389 | 1.000064 | 1.000005 | 0.999999 | 1.000002 | 1.001023 | 1.001062 | 0.999715 | 1.000022 | 1.000212 | 0.999957 | 1.000910 |
| 2017 | 0.999307 | 0.999938 | 0.998319 | 0.999977 | 0.999785 | 0.999920 | 0.999879 | 0.997834 | 0.999975 | 0.998821 | 0.999948 | 0.999661 | 0.999908 | 0.999192 | 1.000096 | 1.000686 | 1.000134 | 1.000089 | 0.999987 | 0.999992 | 1.000009 | 1.001412 | 1.000620 | 0.999193 | 1.000072 | 0.999839 | 0.999999 | 1.000655 |
max
| CF_track,optrev,da,mustrun/CF_track,default,da,mustrun | CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun | Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun | Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun | |||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO |
| yearlmp | ||||||||||||||||||||||||||||
| 2010 | 1.001172 | NaN | 1.000792 | 1.000794 | 1.000564 | 1.000627 | NaN | 1.001016 | NaN | NaN | 1.000706 | 1.000544 | 1.000627 | NaN | 1.001072 | NaN | 1.001377 | 1.001116 | 1.000920 | 1.000875 | NaN | 1.005547 | NaN | NaN | 1.001298 | 1.000996 | 1.003363 | NaN |
| 2011 | 1.000752 | 1.000604 | 1.000835 | 1.000761 | 1.000658 | 1.000732 | NaN | 1.001044 | 1.000943 | 1.000672 | 1.000631 | 1.000683 | 1.000790 | NaN | 1.000690 | 1.004820 | 1.002973 | 1.000876 | 1.001074 | 1.000763 | NaN | 1.001565 | 1.003629 | 1.003724 | 1.000742 | 1.000928 | 1.001388 | NaN |
| 2012 | 1.001394 | 1.000677 | 1.000466 | 1.001388 | 1.000449 | 1.000473 | NaN | 1.000467 | 1.000692 | 1.000496 | 1.001374 | 1.000460 | 1.000475 | NaN | 1.003277 | 1.005793 | 1.001102 | 1.000707 | 1.000427 | 1.000577 | NaN | 1.007822 | 1.019852 | 1.000950 | 1.000996 | 1.000575 | 1.000831 | NaN |
| 2013 | 1.000664 | 1.000958 | 1.000032 | 1.001649 | 1.000309 | 1.000529 | NaN | 1.000619 | 1.000989 | 0.999847 | 1.001649 | 1.000311 | 1.000412 | NaN | 1.000678 | 1.002354 | 1.025779 | 1.001114 | 1.002037 | 1.000801 | NaN | 1.001768 | 1.003758 | 1.020453 | 1.003078 | 1.002317 | 1.003438 | NaN |
| 2014 | 1.000597 | 1.000674 | 0.999892 | 1.000269 | 1.000656 | 1.000427 | NaN | 1.000618 | 1.000593 | 0.999710 | 1.000188 | 1.000880 | 1.000436 | NaN | 1.000690 | 1.004980 | 1.028671 | 1.002310 | 1.018785 | 1.009572 | NaN | 1.001584 | 1.013000 | 1.023783 | 1.002918 | 1.028195 | 1.017517 | NaN |
| 2015 | 1.000596 | 1.000215 | 1.000164 | 1.000819 | 1.000443 | 1.000436 | 1.001821 | 1.000656 | 1.000478 | 1.000146 | 1.000565 | 1.000458 | 1.000375 | 1.001572 | 1.000787 | 1.002898 | 1.005466 | 1.000611 | 1.001643 | 1.001271 | 1.001794 | 1.002090 | 1.013555 | 1.002979 | 1.002814 | 1.004568 | 1.005158 | 1.001186 |
| 2016 | 1.000345 | 1.000269 | 1.000411 | 1.000922 | 1.000303 | 1.000406 | 1.000633 | 1.000005 | 1.000327 | 1.000582 | 1.000957 | 1.000316 | 1.000394 | 1.001466 | 1.000819 | 1.004871 | 1.001526 | 1.000957 | 1.001000 | 1.000468 | 1.001917 | 1.007918 | 1.013189 | 1.000683 | 1.001427 | 1.004024 | 1.001272 | 1.004588 |
| 2017 | 1.000641 | 1.000774 | 1.000206 | 1.001420 | 1.000371 | 1.000549 | 1.001046 | 1.000352 | 1.000698 | 1.000245 | 1.001325 | 1.000398 | 1.000490 | 1.001047 | 1.003549 | 1.007363 | 1.020839 | 1.001177 | 1.001083 | 1.000537 | 1.003041 | 1.006125 | 1.010200 | 1.013903 | 1.002069 | 1.002396 | 1.001522 | 1.005136 |
CF_track,optrev,da,mustrun/CF_track,default,da,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 0.999989 | 1.000036 | 0.999626 | 1.000035 | 0.999946 | 0.999904 | 0.999538 | 0.999243 | 0.998297 | 0.999530 | 0.999986 | 0.999996 | 0.999471 | 0.999381 | 0.999887 | 0.999752 | 0.999610 | 0.999057 | 0.980385 | 0.950009 | 0.994812 | 0.999229 | 0.997968 | 0.999670 | 0.999895 | 1.000032 | 0.999886 | 0.999701 | 0.999972 | 0.999830 | 0.999995 | 1.000044 | 1.000020 | 1.000012 | 0.999745 | 0.973314 | 0.999245 | 0.999795 | 0.999709 | 0.999946 | 1.000041 | 0.999963 | 0.999972 | 0.997467 | 0.999767 | 0.999948 | 0.999896 | 1.000021 | 0.999821 | 0.999800 |
| std | 0.000157 | 0.000164 | 0.000437 | 0.000135 | 0.000156 | 0.000213 | 0.000278 | 0.000488 | 0.000833 | 0.000461 | 0.000232 | 0.000267 | 0.000285 | 0.000368 | 0.000360 | 0.000504 | 0.000434 | 0.000792 | 0.025369 | 0.026693 | 0.009748 | 0.000657 | 0.003020 | 0.000372 | 0.000259 | 0.000239 | 0.000293 | 0.000534 | 0.000218 | 0.000321 | 0.000250 | 0.000194 | 0.000205 | 0.000213 | 0.000382 | 0.034834 | 0.000879 | 0.000231 | 0.000343 | 0.000221 | 0.000204 | 0.000181 | 0.000182 | 0.010511 | 0.000375 | 0.000191 | 0.000234 | 0.000264 | 0.000271 | 0.000393 |
| min | 0.999507 | 0.999010 | 0.996916 | 0.999611 | 0.999337 | 0.999000 | 0.998489 | 0.996503 | 0.996148 | 0.993974 | 0.997341 | 0.996037 | 0.997873 | 0.995912 | 0.993465 | 0.997061 | 0.998374 | 0.995640 | 0.932733 | 0.918200 | 0.934893 | 0.995072 | 0.958328 | 0.998621 | 0.999137 | 0.999346 | 0.999000 | 0.996473 | 0.999076 | 0.998959 | 0.999378 | 0.999145 | 0.998775 | 0.999181 | 0.996215 | 0.915434 | 0.994666 | 0.998922 | 0.998654 | 0.998169 | 0.999293 | 0.998734 | 0.999095 | 0.914935 | 0.995915 | 0.998803 | 0.997910 | 0.999129 | 0.997903 | 0.997729 |
| 2.5% | 0.999701 | 0.999810 | 0.998631 | 0.999817 | 0.999666 | 0.999461 | 0.998916 | 0.998220 | 0.996782 | 0.998574 | 0.999406 | 0.999629 | 0.998932 | 0.998653 | 0.999233 | 0.998804 | 0.998800 | 0.997245 | 0.938519 | 0.926324 | 0.945590 | 0.997722 | 0.995170 | 0.998850 | 0.999373 | 0.999670 | 0.999423 | 0.998418 | 0.999553 | 0.999193 | 0.999570 | 0.999632 | 0.999646 | 0.999584 | 0.998940 | 0.925906 | 0.996719 | 0.999285 | 0.999203 | 0.999479 | 0.999649 | 0.999591 | 0.999586 | 0.992438 | 0.998745 | 0.999536 | 0.999301 | 0.999554 | 0.999160 | 0.998827 |
| 25% | 0.999889 | 0.999940 | 0.999459 | 0.999935 | 0.999844 | 0.999774 | 0.999365 | 0.998920 | 0.997646 | 0.999354 | 0.999901 | 0.999916 | 0.999289 | 0.999196 | 0.999784 | 0.999488 | 0.999303 | 0.998592 | 0.948533 | 0.932030 | 0.995190 | 0.998882 | 0.997684 | 0.999457 | 0.999749 | 0.999914 | 0.999748 | 0.999596 | 0.999846 | 0.999626 | 0.999843 | 0.999924 | 0.999914 | 0.999882 | 0.999630 | 0.926795 | 0.998870 | 0.999701 | 0.999383 | 0.999813 | 0.999908 | 0.999849 | 0.999852 | 0.998607 | 0.999633 | 0.999837 | 0.999798 | 0.999878 | 0.999702 | 0.999648 |
| 50% | 0.999985 | 1.000033 | 0.999685 | 1.000026 | 0.999940 | 0.999927 | 0.999552 | 0.999307 | 0.998260 | 0.999595 | 1.000004 | 1.000019 | 0.999478 | 0.999408 | 0.999938 | 0.999817 | 0.999637 | 0.999169 | 0.997261 | 0.935889 | 0.996990 | 0.999358 | 0.998319 | 0.999743 | 0.999896 | 1.000027 | 0.999882 | 0.999876 | 1.000003 | 0.999861 | 0.999977 | 1.000120 | 1.000037 | 1.000018 | 0.999821 | 0.998162 | 0.999513 | 0.999744 | 0.999785 | 0.999957 | 1.000037 | 0.999965 | 0.999979 | 0.999359 | 0.999850 | 0.999960 | 0.999920 | 0.999989 | 0.999864 | 0.999879 |
| 75% | 1.000091 | 1.000126 | 0.999855 | 1.000119 | 1.000047 | 1.000046 | 0.999742 | 0.999574 | 0.999035 | 0.999780 | 1.000106 | 1.000125 | 0.999668 | 0.999642 | 1.000075 | 1.000058 | 0.999892 | 0.999749 | 0.998555 | 0.952117 | 0.997870 | 0.999700 | 0.998854 | 0.999938 | 1.000029 | 1.000116 | 1.000010 | 1.000043 | 1.000098 | 1.000044 | 1.000122 | 1.000153 | 1.000115 | 1.000199 | 0.999946 | 0.999880 | 0.999679 | 0.999972 | 0.999971 | 1.000095 | 1.000175 | 1.000094 | 1.000110 | 0.999796 | 1.000011 | 1.000084 | 1.000059 | 1.000109 | 0.999997 | 1.000012 |
| 97.5% | 1.000288 | 1.000412 | 1.000368 | 1.000366 | 1.000258 | 1.000266 | 1.000001 | 1.000061 | 0.999684 | 1.000129 | 1.000316 | 1.000274 | 1.000013 | 0.999940 | 1.000288 | 1.000566 | 1.000387 | 1.000203 | 0.999769 | 0.998058 | 0.999299 | 1.000107 | 0.999878 | 1.000297 | 1.000468 | 1.000480 | 1.000260 | 1.000207 | 1.000395 | 1.000504 | 1.000520 | 1.000395 | 1.000439 | 1.000410 | 1.000230 | 1.000441 | 1.000208 | 1.000195 | 1.000227 | 1.000349 | 1.000461 | 1.000276 | 1.000276 | 1.000184 | 1.000221 | 1.000243 | 1.000231 | 1.000667 | 1.000228 | 1.000558 |
| max | 1.001172 | 1.000752 | 1.001394 | 1.000664 | 1.000597 | 1.000596 | 1.000345 | 1.000641 | 1.000604 | 1.000677 | 1.000958 | 1.000674 | 1.000215 | 1.000269 | 1.000774 | 1.000792 | 1.000835 | 1.000466 | 1.000032 | 0.999892 | 1.000164 | 1.000411 | 1.000206 | 1.000794 | 1.000761 | 1.001388 | 1.001649 | 1.000269 | 1.000819 | 1.000922 | 1.001420 | 1.000564 | 1.000658 | 1.000449 | 1.000309 | 1.000656 | 1.000443 | 1.000303 | 1.000371 | 1.000627 | 1.000732 | 1.000473 | 1.000529 | 1.000427 | 1.000436 | 1.000406 | 1.000549 | 1.001821 | 1.000633 | 1.001046 |
CF_track,optrev,rt,mustrun/CF_track,default,rt,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 0.999669 | 0.999969 | 0.999658 | 0.999953 | 0.999774 | 0.999888 | 0.997326 | 0.997816 | 0.999640 | 0.999729 | 0.999788 | 0.999919 | 0.999954 | 0.999424 | 0.999883 | NaN | 0.999211 | 0.999139 | 0.991287 | 0.946429 | 0.998033 | 0.999637 | 0.997008 | 0.999528 | 0.999729 | 0.999939 | 0.999669 | 0.999463 | 0.999770 | 0.999782 | 0.999905 | 0.999944 | 1.000030 | 1.000040 | 0.999696 | 0.960074 | 0.999365 | 0.999188 | 0.999598 | 0.999843 | 1.000005 | 0.999909 | 0.999756 | 0.996606 | 0.999490 | 0.999890 | 0.999855 | 1.000063 | 0.998634 | 0.999237 |
| std | 0.004982 | 0.000238 | 0.001726 | 0.000172 | 0.000225 | 0.000285 | 0.001135 | 0.000892 | 0.000542 | 0.000997 | 0.000341 | 0.000485 | 0.000463 | 0.000833 | 0.000565 | NaN | 0.000545 | 0.000777 | 0.014686 | 0.024231 | 0.005305 | 0.000565 | 0.008189 | 0.000545 | 0.000370 | 0.000324 | 0.000505 | 0.000685 | 0.000379 | 0.000370 | 0.000339 | 0.000266 | 0.000204 | 0.000193 | 0.000343 | 0.035951 | 0.000979 | 0.002388 | 0.000473 | 0.000321 | 0.000236 | 0.000228 | 0.000569 | 0.006479 | 0.000658 | 0.000240 | 0.000448 | 0.000301 | 0.001136 | 0.000722 |
| min | 0.884374 | 0.998256 | 0.960139 | 0.998812 | 0.998682 | 0.998280 | 0.993306 | 0.993499 | 0.997640 | 0.984333 | 0.997181 | 0.991247 | 0.990183 | 0.983468 | 0.990033 | NaN | 0.997678 | 0.995294 | 0.937190 | 0.919893 | 0.943020 | 0.995072 | 0.948890 | 0.997209 | 0.998377 | 0.998309 | 0.997021 | 0.995967 | 0.997686 | 0.997532 | 0.998459 | 0.998906 | 0.999256 | 0.999461 | 0.997692 | 0.914034 | 0.992289 | 0.985067 | 0.996839 | 0.997506 | 0.999044 | 0.998551 | 0.995776 | 0.916809 | 0.989978 | 0.997567 | 0.989016 | 0.998147 | 0.993883 | 0.994979 |
| 2.5% | 0.999191 | 0.999447 | 0.998218 | 0.999620 | 0.999330 | 0.999152 | 0.994729 | 0.996035 | 0.998335 | 0.998386 | 0.998832 | 0.999062 | 0.999378 | 0.998364 | 0.998995 | NaN | 0.998111 | 0.997404 | 0.946293 | 0.926118 | 0.994446 | 0.998318 | 0.959299 | 0.998121 | 0.998851 | 0.999292 | 0.998643 | 0.997697 | 0.998885 | 0.999049 | 0.999090 | 0.999396 | 0.999560 | 0.999625 | 0.998871 | 0.919705 | 0.996363 | 0.989948 | 0.999008 | 0.999120 | 0.999508 | 0.999398 | 0.997752 | 0.988252 | 0.997862 | 0.999300 | 0.999096 | 0.999464 | 0.996151 | 0.997553 |
| 25% | 0.999794 | 0.999879 | 0.999622 | 0.999857 | 0.999627 | 0.999764 | 0.996661 | 0.997235 | 0.999338 | 0.999671 | 0.999682 | 0.999877 | 0.999903 | 0.999271 | 0.999806 | NaN | 0.998849 | 0.998686 | 0.994612 | 0.931881 | 0.997925 | 0.999437 | 0.998163 | 0.999321 | 0.999526 | 0.999853 | 0.999500 | 0.999155 | 0.999611 | 0.999623 | 0.999800 | 0.999797 | 0.999925 | 0.999893 | 0.999567 | 0.926496 | 0.998962 | 0.999607 | 0.999231 | 0.999673 | 0.999864 | 0.999802 | 0.999697 | 0.995598 | 0.999267 | 0.999775 | 0.999769 | 0.999907 | 0.997750 | 0.998831 |
| 50% | 0.999930 | 0.999993 | 0.999886 | 0.999967 | 0.999779 | 0.999944 | 0.997386 | 0.997834 | 0.999827 | 0.999874 | 0.999841 | 0.999990 | 1.000007 | 0.999543 | 0.999975 | NaN | 0.999259 | 0.999282 | 0.996403 | 0.935391 | 0.998985 | 0.999770 | 0.998821 | 0.999645 | 0.999819 | 0.999987 | 0.999726 | 0.999751 | 0.999854 | 0.999854 | 0.999948 | 1.000018 | 1.000029 | 1.000080 | 0.999714 | 0.931320 | 0.999666 | 0.999680 | 0.999661 | 0.999884 | 1.000008 | 0.999931 | 0.999879 | 0.998010 | 0.999671 | 0.999920 | 0.999908 | 1.000085 | 0.998704 | 0.999192 |
| 75% | 1.000057 | 1.000097 | 1.000023 | 1.000059 | 0.999919 | 1.000073 | 0.998117 | 0.998486 | 1.000043 | 1.000032 | 0.999973 | 1.000101 | 1.000109 | 0.999745 | 1.000113 | NaN | 0.999535 | 0.999766 | 0.997252 | 0.946606 | 0.999514 | 0.999980 | 0.999288 | 0.999886 | 0.999975 | 1.000097 | 0.999924 | 0.999929 | 1.000022 | 1.000007 | 1.000118 | 1.000111 | 1.000192 | 1.000204 | 0.999913 | 0.999221 | 0.999974 | 0.999931 | 0.999952 | 1.000066 | 1.000164 | 1.000064 | 1.000038 | 0.999390 | 0.999915 | 1.000054 | 1.000052 | 1.000232 | 0.999684 | 0.999794 |
| 97.5% | 1.000260 | 1.000355 | 1.000220 | 1.000309 | 1.000172 | 1.000274 | 0.999322 | 0.999333 | 1.000282 | 1.000275 | 1.000242 | 1.000240 | 1.000238 | 1.000079 | 1.000304 | NaN | 1.000187 | 1.000181 | 0.999339 | 0.997715 | 0.999997 | 1.000263 | 1.000035 | 1.000303 | 1.000283 | 1.000286 | 1.000228 | 1.000134 | 1.000201 | 1.000315 | 1.000278 | 1.000387 | 1.000440 | 1.000392 | 1.000257 | 1.000416 | 1.000235 | 1.000185 | 1.000222 | 1.000338 | 1.000455 | 1.000242 | 1.000257 | 1.000081 | 1.000193 | 1.000223 | 1.000227 | 1.000634 | 1.000218 | 1.000543 |
| max | 1.001016 | 1.001044 | 1.000467 | 1.000619 | 1.000618 | 1.000656 | 1.000005 | 1.000352 | 1.000943 | 1.000692 | 1.000989 | 1.000593 | 1.000478 | 1.000327 | 1.000698 | NaN | 1.000672 | 1.000496 | 0.999847 | 0.999710 | 1.000146 | 1.000582 | 1.000245 | 1.000706 | 1.000631 | 1.001374 | 1.001649 | 1.000188 | 1.000565 | 1.000957 | 1.001325 | 1.000544 | 1.000683 | 1.000460 | 1.000311 | 1.000880 | 1.000458 | 1.000316 | 1.000398 | 1.000627 | 1.000790 | 1.000475 | 1.000412 | 1.000436 | 1.000375 | 1.000394 | 1.000490 | 1.001572 | 1.001466 | 1.001047 |
Rev_track,optrev,da,mustrun/Rev_track,default,da,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2118.000000 | 2121.000000 | 2150.000000 | 2204.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1559.000000 | 1563.000000 | 1567.000000 | 1569.000000 | 1570.000000 | 1570.000000 | 1563.000000 | 409.000000 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.000039 | 1.000046 | 1.000427 | 1.000013 | 1.000105 | 1.000094 | 1.000028 | 1.000141 | 1.003246 | 1.001402 | 1.000148 | 1.000240 | 1.001117 | 1.001006 | 1.000764 | 1.000208 | 1.000573 | 0.999847 | 1.003006 | 1.006034 | 0.998664 | 1.000331 | 1.000162 | 1.000053 | 1.000062 | 1.000008 | 1.000089 | 1.000266 | 1.000025 | 1.000107 | 1.000145 | 1.000174 | 1.000128 | 1.000064 | 1.000197 | 0.996842 | 0.999694 | 1.000093 | 0.999952 | 1.000064 | 1.000094 | 1.000015 | 1.000008 | 1.001401 | 1.000087 | 0.999989 | 0.999967 | 1.000074 | 1.000062 | 1.000298 |
| std | 0.000122 | 0.000097 | 0.000486 | 0.000090 | 0.000134 | 0.000173 | 0.000217 | 0.000311 | 0.000742 | 0.000504 | 0.000203 | 0.000311 | 0.000307 | 0.000315 | 0.000451 | 0.000491 | 0.000434 | 0.000560 | 0.005256 | 0.007055 | 0.003073 | 0.000500 | 0.000914 | 0.000233 | 0.000152 | 0.000143 | 0.000228 | 0.000502 | 0.000165 | 0.000236 | 0.000240 | 0.000244 | 0.000206 | 0.000152 | 0.000365 | 0.008575 | 0.000608 | 0.000267 | 0.000260 | 0.000229 | 0.000195 | 0.000138 | 0.000109 | 0.002228 | 0.000274 | 0.000121 | 0.000148 | 0.000240 | 0.000234 | 0.000652 |
| min | 0.999513 | 0.999418 | 0.999624 | 0.999638 | 0.999775 | 0.999570 | 0.999124 | 0.999107 | 1.000480 | 1.000080 | 0.999857 | 0.999438 | 1.000299 | 1.000051 | 0.999852 | 0.998227 | 0.999381 | 0.997632 | 0.983600 | 0.974291 | 0.966893 | 0.998250 | 0.997615 | 0.999086 | 0.999489 | 0.999330 | 0.999120 | 0.998374 | 0.998852 | 0.999091 | 0.999455 | 0.999364 | 0.999577 | 0.999375 | 0.999314 | 0.965441 | 0.998025 | 0.999539 | 0.999433 | 0.998933 | 0.999193 | 0.999291 | 0.999298 | 0.973155 | 0.998083 | 0.999070 | 0.998782 | 0.999670 | 0.999298 | 0.999617 |
| 2.5% | 0.999818 | 0.999917 | 0.999903 | 0.999861 | 0.999927 | 0.999798 | 0.999651 | 0.999705 | 1.001813 | 1.000647 | 0.999965 | 0.999998 | 1.000647 | 1.000513 | 1.000292 | 0.999214 | 0.999973 | 0.998596 | 0.990798 | 0.993690 | 0.988427 | 0.999126 | 0.998939 | 0.999651 | 0.999747 | 0.999691 | 0.999738 | 0.999345 | 0.999648 | 0.999680 | 0.999789 | 0.999823 | 0.999746 | 0.999746 | 0.999673 | 0.985855 | 0.998345 | 0.999771 | 0.999491 | 0.999658 | 0.999787 | 0.999723 | 0.999770 | 0.999291 | 0.999390 | 0.999687 | 0.999549 | 0.999855 | 0.999834 | 0.999863 |
| 25% | 0.999985 | 0.999998 | 1.000075 | 0.999967 | 1.000011 | 0.999978 | 0.999894 | 0.999981 | 1.002699 | 1.001142 | 1.000060 | 1.000132 | 1.000905 | 1.000828 | 1.000534 | 0.999975 | 1.000305 | 0.999527 | 1.001058 | 1.002899 | 0.998044 | 1.000026 | 0.999787 | 0.999949 | 0.999977 | 0.999970 | 0.999980 | 0.999999 | 0.999974 | 0.999987 | 0.999997 | 0.999990 | 0.999995 | 0.999982 | 1.000025 | 0.987437 | 0.999323 | 0.999921 | 0.999830 | 0.999948 | 0.999984 | 0.999964 | 0.999966 | 1.000702 | 0.999981 | 0.999949 | 0.999928 | 0.999973 | 0.999982 | 0.999960 |
| 50% | 1.000017 | 1.000026 | 1.000277 | 1.000001 | 1.000070 | 1.000084 | 1.000000 | 1.000096 | 1.003243 | 1.001375 | 1.000107 | 1.000191 | 1.001089 | 1.000978 | 1.000686 | 1.000232 | 1.000502 | 0.999896 | 1.002520 | 1.005401 | 0.999301 | 1.000389 | 1.000134 | 1.000038 | 1.000039 | 1.000008 | 1.000059 | 1.000050 | 1.000033 | 1.000064 | 1.000089 | 1.000101 | 1.000075 | 1.000069 | 1.000127 | 1.000367 | 0.999705 | 1.000005 | 0.999987 | 1.000011 | 1.000030 | 1.000002 | 1.000009 | 1.001401 | 1.000088 | 0.999999 | 0.999992 | 1.000004 | 1.000002 | 1.000009 |
| 75% | 1.000078 | 1.000068 | 1.000619 | 1.000045 | 1.000178 | 1.000192 | 1.000120 | 1.000226 | 1.003915 | 1.001634 | 1.000186 | 1.000268 | 1.001269 | 1.001120 | 1.000881 | 1.000430 | 1.000811 | 1.000145 | 1.004620 | 1.008337 | 1.000236 | 1.000663 | 1.000464 | 1.000150 | 1.000133 | 1.000057 | 1.000176 | 1.000509 | 1.000101 | 1.000216 | 1.000264 | 1.000328 | 1.000260 | 1.000153 | 1.000297 | 1.002458 | 0.999994 | 1.000213 | 1.000078 | 1.000150 | 1.000175 | 1.000052 | 1.000055 | 1.002120 | 1.000250 | 1.000046 | 1.000032 | 1.000051 | 1.000064 | 1.000197 |
| 97.5% | 1.000335 | 1.000313 | 1.001623 | 1.000211 | 1.000453 | 1.000475 | 1.000577 | 1.000955 | 1.004391 | 1.002098 | 1.000789 | 1.000603 | 1.001830 | 1.001869 | 1.001820 | 1.001142 | 1.001551 | 1.000819 | 1.017351 | 1.023781 | 1.001801 | 1.001202 | 1.001347 | 1.000496 | 1.000387 | 1.000328 | 1.000589 | 1.001550 | 1.000374 | 1.000576 | 1.000834 | 1.000619 | 1.000642 | 1.000401 | 1.001167 | 1.006759 | 1.001377 | 1.000860 | 1.000457 | 1.000640 | 1.000588 | 1.000368 | 1.000250 | 1.005196 | 1.000532 | 1.000217 | 1.000212 | 1.000729 | 1.000776 | 1.002262 |
| max | 1.001072 | 1.000690 | 1.003277 | 1.000678 | 1.000690 | 1.000787 | 1.000819 | 1.003549 | 1.004820 | 1.005793 | 1.002354 | 1.004980 | 1.002898 | 1.004871 | 1.007363 | 1.001377 | 1.002973 | 1.001102 | 1.025779 | 1.028671 | 1.005466 | 1.001526 | 1.020839 | 1.001116 | 1.000876 | 1.000707 | 1.001114 | 1.002310 | 1.000611 | 1.000957 | 1.001177 | 1.000920 | 1.001074 | 1.000427 | 1.002037 | 1.018785 | 1.001643 | 1.001000 | 1.001083 | 1.000875 | 1.000763 | 1.000577 | 1.000801 | 1.009572 | 1.001271 | 1.000468 | 1.000537 | 1.001794 | 1.001917 | 1.003041 |
Rev_track,optrev,rt,mustrun/Rev_track,default,rt,mustrun
| ISOwecc | CAISO | ERCOT | ISONE | MISO | NYISO | PJM | WECC -CAISO | |||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yearlmp | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2015 | 2016 | 2017 |
| count | 2114.000000 | 2121.000000 | 2150.000000 | 2205.000000 | 2234.000000 | 2236.000000 | 2237.000000 | 2209.000000 | 1560.000000 | 1563.000000 | 1567.000000 | 1570.000000 | 1572.000000 | 1570.000000 | 1564.000000 | 0.0 | 437.000000 | 499.000000 | 593.000000 | 612.000000 | 746.000000 | 829.000000 | 966.000000 | 179.000000 | 192.000000 | 197.000000 | 200.000000 | 368.000000 | 370.000000 | 386.000000 | 378.000000 | 402.000000 | 412.000000 | 424.000000 | 430.000000 | 434.000000 | 435.000000 | 436.000000 | 436.000000 | 4288.000000 | 4366.000000 | 4967.000000 | 5044.000000 | 4936.000000 | 4857.000000 | 4741.000000 | 4686.000000 | 1049.000000 | 1824.000000 | 3461.000000 |
| mean | 1.000039 | 1.000059 | 1.000364 | 1.000049 | 1.000418 | 1.000170 | 1.001361 | 1.001523 | 1.001475 | 1.001200 | 1.000779 | 1.000030 | 1.000185 | 1.001243 | 1.000899 | NaN | 1.000937 | 0.999840 | 1.004304 | 1.002358 | 0.998684 | 0.999551 | 0.999056 | 1.000090 | 1.000097 | 1.000045 | 1.000150 | 1.000606 | 1.000175 | 1.000064 | 1.000166 | 1.000079 | 1.000195 | 1.000057 | 0.999995 | 0.999138 | 1.000129 | 1.000421 | 0.999808 | 1.000066 | 1.000024 | 0.999996 | 1.000134 | 1.004541 | 1.000158 | 0.999921 | 0.999990 | 1.000250 | 1.000867 | 1.000688 |
| std | 0.000290 | 0.000194 | 0.000767 | 0.000189 | 0.000277 | 0.000244 | 0.001318 | 0.000750 | 0.000964 | 0.001128 | 0.000418 | 0.000552 | 0.000648 | 0.000928 | 0.000912 | NaN | 0.000581 | 0.000530 | 0.003802 | 0.007336 | 0.002826 | 0.000667 | 0.001964 | 0.000319 | 0.000216 | 0.000265 | 0.000446 | 0.000692 | 0.000435 | 0.000272 | 0.000405 | 0.000275 | 0.000315 | 0.000136 | 0.000466 | 0.011324 | 0.000655 | 0.000882 | 0.000477 | 0.000305 | 0.000172 | 0.000180 | 0.000449 | 0.003077 | 0.000509 | 0.000231 | 0.000523 | 0.000247 | 0.000958 | 0.000635 |
| min | 0.997717 | 0.999441 | 0.993888 | 0.998633 | 0.999606 | 0.999220 | 0.996528 | 0.999546 | 0.999200 | 0.993535 | 0.998885 | 0.998074 | 0.995243 | 0.999125 | 0.995641 | NaN | 0.999329 | 0.997026 | 0.980139 | 0.969552 | 0.971525 | 0.994774 | 0.974714 | 0.998642 | 0.999564 | 0.998327 | 0.999256 | 0.999450 | 0.999097 | 0.998610 | 0.998632 | 0.999348 | 0.998963 | 0.999630 | 0.998927 | 0.949653 | 0.997813 | 0.997072 | 0.997280 | 0.998655 | 0.999037 | 0.998784 | 0.997573 | 0.978566 | 0.993104 | 0.998444 | 0.988573 | 0.998766 | 0.995179 | 0.996872 |
| 2.5% | 0.999596 | 0.999762 | 0.999648 | 0.999746 | 0.999992 | 0.999772 | 0.999163 | 1.000470 | 0.999857 | 1.000036 | 1.000061 | 0.999577 | 0.999847 | 1.000420 | 1.000081 | NaN | 1.000027 | 0.998743 | 0.997771 | 0.987178 | 0.995138 | 0.998132 | 0.996833 | 0.999527 | 0.999721 | 0.999474 | 0.999441 | 0.999831 | 0.999625 | 0.999574 | 0.999523 | 0.999732 | 0.999620 | 0.999777 | 0.999387 | 0.970272 | 0.998647 | 0.998925 | 0.999048 | 0.999582 | 0.999690 | 0.999581 | 0.999620 | 0.999868 | 0.999211 | 0.999374 | 0.999344 | 0.999750 | 0.999090 | 0.999662 |
| 25% | 0.999923 | 0.999968 | 0.999974 | 0.999962 | 1.000229 | 1.000014 | 1.000402 | 1.001010 | 1.000754 | 1.000926 | 1.000611 | 0.999960 | 1.000033 | 1.000886 | 1.000428 | NaN | 1.000567 | 0.999571 | 1.002966 | 0.998801 | 0.998340 | 0.999286 | 0.998607 | 0.999968 | 0.999947 | 0.999956 | 0.999963 | 1.000068 | 0.999990 | 0.999929 | 0.999971 | 0.999906 | 0.999996 | 0.999994 | 0.999576 | 0.995989 | 0.999851 | 0.999936 | 0.999559 | 0.999921 | 0.999943 | 0.999934 | 0.999962 | 1.002114 | 0.999978 | 0.999818 | 0.999853 | 1.000087 | 1.000046 | 1.000358 |
| 50% | 1.000006 | 1.000024 | 1.000042 | 1.000013 | 1.000377 | 1.000120 | 1.001023 | 1.001412 | 1.001344 | 1.001156 | 1.000766 | 1.000007 | 1.000103 | 1.001062 | 1.000620 | NaN | 1.000912 | 0.999907 | 1.004215 | 1.003508 | 0.999214 | 0.999715 | 0.999193 | 1.000060 | 1.000052 | 1.000031 | 1.000103 | 1.000307 | 1.000117 | 1.000022 | 1.000072 | 1.000019 | 1.000129 | 1.000046 | 0.999984 | 0.999673 | 1.000067 | 1.000212 | 0.999839 | 1.000014 | 1.000005 | 0.999999 | 1.000041 | 1.004511 | 1.000146 | 0.999957 | 0.999999 | 1.000271 | 1.000910 | 1.000655 |
| 75% | 1.000101 | 1.000106 | 1.000582 | 1.000108 | 1.000566 | 1.000285 | 1.002433 | 1.001901 | 1.002350 | 1.001392 | 1.000905 | 1.000051 | 1.000200 | 1.001317 | 1.001317 | NaN | 1.001290 | 1.000161 | 1.005822 | 1.005644 | 0.999848 | 0.999988 | 0.999729 | 1.000201 | 1.000238 | 1.000164 | 1.000266 | 1.001029 | 1.000271 | 1.000170 | 1.000256 | 1.000148 | 1.000358 | 1.000133 | 1.000264 | 1.005390 | 1.000413 | 1.001025 | 1.000014 | 1.000190 | 1.000101 | 1.000055 | 1.000171 | 1.006254 | 1.000328 | 1.000039 | 1.000144 | 1.000386 | 1.001664 | 1.000876 |
| 97.5% | 1.000673 | 1.000592 | 1.002819 | 1.000583 | 1.001075 | 1.000695 | 1.003724 | 1.003406 | 1.003220 | 1.002498 | 1.001870 | 1.000482 | 1.000795 | 1.003005 | 1.002888 | NaN | 1.002173 | 1.000673 | 1.011365 | 1.016771 | 1.000410 | 1.000349 | 1.000815 | 1.000800 | 1.000620 | 1.000483 | 1.001543 | 1.002097 | 1.001843 | 1.000598 | 1.001256 | 1.000847 | 1.000768 | 1.000363 | 1.000950 | 1.015298 | 1.001381 | 1.002382 | 1.000938 | 1.000657 | 1.000433 | 1.000418 | 1.001533 | 1.010969 | 1.001232 | 1.000304 | 1.000745 | 1.000805 | 1.002332 | 1.002202 |
| max | 1.005547 | 1.001565 | 1.007822 | 1.001768 | 1.001584 | 1.002090 | 1.007918 | 1.006125 | 1.003629 | 1.019852 | 1.003758 | 1.013000 | 1.013555 | 1.013189 | 1.010200 | NaN | 1.003724 | 1.000950 | 1.020453 | 1.023783 | 1.002979 | 1.000683 | 1.013903 | 1.001298 | 1.000742 | 1.000996 | 1.003078 | 1.002918 | 1.002814 | 1.001427 | 1.002069 | 1.000996 | 1.000928 | 1.000575 | 1.002317 | 1.028195 | 1.004568 | 1.004024 | 1.002396 | 1.003363 | 1.001388 | 1.000831 | 1.003438 | 1.017517 | 1.005158 | 1.001272 | 1.001522 | 1.001186 | 1.004588 | 1.005136 |
dfmap = dfplot.merge(
dfin[['ISO:Node','yearlmp','Latitude','Longitude']].drop_duplicates(['ISO:Node','yearlmp']),
on=['ISO:Node','yearlmp'], how='left'
).copy()
maplabel = {
'OptRev_Azimuth(rt)(mustrun)': 'Azimuth [°]',
'OptRev_Azimuth(rt)(curtail)': 'Azimuth [°]',
'OptRev_Tilt(rt)(mustrun)': 'Tilt [°]',
'OptRev_Tilt(rt)(curtail)': 'Tilt [°]',
'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': 'Revenue ratio\ntrack/fixed',
'Rev_curtail/Rev_mustrun(fixed)(rt)': 'Revenue ratio\ncurtail/must-run',
'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun': 'Revenue ratio\nRevenue-opt/CF-opt'
}
zrange = {
'OptRev_Azimuth(rt)(mustrun)': [90,270],
'OptRev_Tilt(rt)(mustrun)': [15,57],
'OptRev_Azimuth(rt)(curtail)': [120,240],
'OptRev_Tilt(rt)(curtail)': [15,47],
'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': [1., 1.7],
'Rev_curtail/Rev_mustrun(fixed)(rt)': [0.99,1.32],
'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun': [0.99,1.37],
}
zticks = {
'OptRev_Azimuth(rt)(mustrun)': [90,135,180,225,270],
'OptRev_Tilt(rt)(mustrun)': [20,30,40,50,],
'OptRev_Azimuth(rt)(curtail)': [120,150,180,210,240],
'OptRev_Tilt(rt)(curtail)': [15,25,35,45],
'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': list(np.arange(1,1.71,0.1)),
'Rev_curtail/Rev_mustrun(fixed)(rt)': [1.,1.1,1.2,1.3],
'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun': [1.,1.1,1.2,1.3]
}
cmap = plt.cm.coolwarm#{
# 'OptRev_Azimuth(rt)(mustrun)': plt.cm.coolwarm,
# 'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun': plt.cm.coolwarm,
# 'Rev_curtail/Rev_mustrun(fixed)(rt)': plt.cm.coolwarm,
# }
data = [
# 'OptRev_Azimuth(rt)(mustrun)',
# 'OptRev_Tilt(rt)(mustrun)',
'OptRev_Azimuth(rt)(curtail)',
'OptRev_Tilt(rt)(curtail)',
'Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun',
'Rev_curtail/Rev_mustrun(fixed)(rt)',
'Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun',
]
for datum in data:
print('{}\n{}\n{}'.format('#'*30,datum,'#'*30))
for year in range(2010,2018):
plt.close()
f,ax = pvvm.plots.plotusascattermap(
dfmap.loc[dfmap.yearlmp==year], datum,
zrange=zrange[datum],
# colorbartitle=maplabel[datum],
colorbarhist=False,
cmap=cmap,
)
cbar, chist = pvvm.plots.addcolorbarhist(
f=f, ax0=ax, data=dfmap.loc[dfmap.yearlmp==year,datum],
title=maplabel[datum], cmap=cmap,
vmin=zrange[datum][0], vmax=zrange[datum][1]
)
cbar.yaxis.set_ticks(zticks[datum])
ax.set_title(year, y=0.93, fontsize='x-large', weight='bold')
ax.annotate(
datum.replace('/','\n/ '),
xy=(0.03,0.05), xycoords='axes fraction',
ha='left', va='bottom', fontsize='medium')
plt.show()
############################## OptRev_Azimuth(rt)(curtail) ##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
############################## OptRev_Tilt(rt)(curtail) ##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
############################## Revenue_track(def)/fixed(optcf)_hist,rt,curtail,baselinemustrun ##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
############################## Rev_curtail/Rev_mustrun(fixed)(rt) ##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
############################## Rev_OptRev/OptCF_hist,rt,f,curtail,baselinemustrun ##############################
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:1125: MatplotlibDeprecationWarning: The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead. m_in.readshapefile(mappath, 'States', drawbounds=False)
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
'CF_curtail-CF_mustrun(fixed)(da)',
'CF_curtail-CF_mustrun(fixed)(rt)',
'Rev_curtail-Rev_mustrun(fixed)(da)',
'Rev_curtail-Rev_mustrun(fixed)(rt)',
]
colindex = [0, 0, 1, 1]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))
### Column-indexed parameters
ncols = 2
ylim = [
[-0.08, 0.01],
[-1, 15],
]
ylabel = [
'Capacity Factor',
'Revenue [$/kWac-yr]',
]
note = [
'',
'',
]
y1 = 1 # 1.2 if using note
y2 = 1.04 # 1.07 if using note
gridspec_kw = {'width_ratios': [2, 2]}
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
# format_axes=False,
)
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
ax[(row,col)].set_xlim(2009.4,2018)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
# ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.1))
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
ax[(row,col)].axhline(0, lw=0.25, c='0.5')
ax[(0,0)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0))
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(-1,0)].legend(
handles=patches, loc='lower left', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Difference, curtailable – must-run, fixed', weight='bold', y=y2, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]
########## Starting absolute values for fixed CF-opt
### Data-indexed parameters
data = [
'CapacityFactor_track(def)-fixed(optcf)_hist,da,mustrun',
'CapacityFactor_track(def)-fixed(optcf)_hist,da,curtail,baselinemustrun',
'CapacityFactor_track(def)-fixed(optcf)_hist,rt,curtail,baselinemustrun',
'Revenue_track(def)-fixed(optcf)_hist,da,mustrun',
'Revenue_track(def)-fixed(optcf)_hist,rt,mustrun',
'Revenue_track(def)-fixed(optcf)_hist,da,curtail,baselinemustrun',
'Revenue_track(def)-fixed(optcf)_hist,rt,curtail,baselinemustrun',
]
colindex = [0, 1, 1, 2, 2, 3, 3]
colindex = dict(zip(data, colindex))
direction = ['right','left','right','left','right','left','right']
direction = dict(zip(data, direction))
color = [mc['tmy'],mc['da'],mc['rt'],mc['da'],mc['rt'],mc['da'],mc['rt']]
color = dict(zip(data, color))
squeeze = [0.7, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35]
squeeze = dict(zip(data, squeeze))
### Column-indexed parameters
ncols = 4
ylim = [
[-0.03,0.08],
[-0.03,0.08],
[-3,40],
[-3,40],
]
ylabel = [
'Capacity Factor',
'Capacity Factor',
'Revenue [$/kWac-yr]',
'Revenue [$/kWac-yr]',
]
note = [
'(must-run)',
'(curtailable)',
'(must-run)',
'(curtailable)',
]
y1 = 1.2 # 1.2 if using note, 1 if not
y2 = 1.07 # 1.07 if using note, 1.05 if not
gridspec_kw = {'width_ratios': [1, 2, 2, 2]}
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex=True,sharey='col', gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe, bootstrap=bootstrap, density=True,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
# medianmarker='_', mediansize=10, medianfacecolor='k'
)
# ### NEW: Add revenue cutoff line
# if datum.startswith('Revenue') and (',da,' in datum):
# ax[(row,colindex[datum])].axhline(1.066, lw=0.25, ls=(0,(6,6)), c='0.5')
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
ax[(row,col)].set_xlim(2009.4,2018)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
# ax[(row,col)].yaxis.set_major_locator(MultipleLocator(0.2))
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(2))
ax[(row,col)].axhline(0, lw=0.25, c='0.5')
ax[(0,0)].yaxis.set_major_locator(MultipleLocator(0.05))
ax[(0,1)].yaxis.set_major_locator(MultipleLocator(0.05))
ax[(0,2)].yaxis.set_major_locator(MultipleLocator(10))
ax[(0,3)].yaxis.set_major_locator(MultipleLocator(10))
ax[(0,0)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0,))
ax[(0,1)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0,))
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(-1,-1)].legend(
handles=patches, loc='upper right', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# # plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Difference, 1-ax track – fixed', weight='bold', y=y2, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]
########## Baseline = mustrun
### Data-indexed parameters
data = [
'CF_OptRev-OptCF_hist,da,f,mustrun',
'CF_OptRev-OptCF_hist,rt,f,mustrun',
'CF_OptRev-OptCF_hist,da,f,curtail,baselinemustrun',
'CF_OptRev-OptCF_hist,rt,f,curtail,baselinemustrun',
'Rev_OptRev-OptCF_hist,da,f,mustrun',
'Rev_OptRev-OptCF_hist,rt,f,mustrun',
'Rev_OptRev-OptCF_hist,da,f,curtail,baselinemustrun',
'Rev_OptRev-OptCF_hist,rt,f,curtail,baselinemustrun',
]
colindex = [0, 0, 1, 1, 2, 2, 3, 3,]
colindex = dict(zip(data, colindex))
direction = ['left','right','left','right',
'left','right','left','right',]
direction = dict(zip(data, direction))
color = [mc['da'],mc['rt'],mc['da'],mc['rt'],
mc['da'],mc['rt'],mc['da'],mc['rt'],]
color = dict(zip(data, color))
squeeze = [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,]
squeeze = dict(zip(data, squeeze))
plotcols = [slice(None),slice(None),slice(None),slice(None),
slice(None),slice(None),slice(None),slice(None),]
plotcols = dict(zip(data, plotcols))
### Column-indexed parameters
ylim = [
[-0.09, 0.01],
[-0.09, 0.01],
[-1,20],
[-1,20],
]
xlim = [
[2009.4, 2018],
[2009.4, 2018],
[2009.4, 2018],
[2009.4, 2018],
]
majlocs = [0.05, 0.05, 10, 10]
minlocs = [2, 2, 2, 2,]
ylabel = [
'Capacity Factor',
'Capacity Factor',
'Revenue [$/kWac-yr]',
'Revenue [$/kWac-yr]',
]
note = [
'(must-run)',
'(curtailable)',
'(must-run)',
'(curtailable)',
]
y1 = 1.2 # 1.2 if using note, 1 if no note
y2 = 1.07 # 1.07 if using note, 1.04 if no note
gridspec_kw = {'width_ratios': [2, 2, 2, 2,]}#, 'wspace':0.4}
ncols = len(gridspec_kw['width_ratios'])
### Plot it
plt.close()
f,ax = plt.subplots(6,ncols,sharex='col',sharey='col', gridspec_kw=gridspec_kw,
figsize=(sum(gridspec_kw['width_ratios'])*12/7, figheight),
)
for row, iso in enumerate(isos):
for datum in data:
dfframe = (dfplot.loc[dfplot.ISOwecc==iso]
.pivot(index='ISO:Node',columns='yearlmp',values=datum))
pvvm.plots.plotquarthist(
ax=ax[(row,colindex[datum])], dfplot=dfframe[plotcols[datum]],
density=True, bootstrap=bootstrap,
histcolor=color[datum], hist_range=ylim[colindex[datum]],
direction=direction[datum], squeeze=squeeze[datum],
quartpad=(-0.1 if direction[datum] == 'left' else 0.1),
histpad=(-0.15 if direction[datum] == 'left' else 0.15),
format_axes=False,
)
### Format axis
for row, iso in enumerate(isos):
for col in range(ncols):
ax[(row,0)].set_ylabel(iso, weight='bold', rotation=0, labelpad=30)
### x ticks
ax[(row,col)].set_xticks([2010,2014])
ax[(row,col)].set_xticklabels(
['2010','2014'], rotation=0, ha='center')
ax[(row,col)].xaxis.set_minor_locator(AutoMinorLocator(4))
ax[(row,col)].set_xlim(*xlim[col])
### Add title
ax[(0,col)].set_title(ylabel[col], weight='bold', y=y1, size='x-large')
### Add annotation
ax[(0,col)].annotate(
note[col], xy=(0.5,1.05), xycoords='axes fraction',
ha='center', va='bottom', fontsize='large')
### Format axis
ax[(row,col)].set_ylim(*ylim[col])
# ax[(row,col)].yaxis.set_major_locator(MultipleLocator(majlocs[col]))
ax[(row,col)].yaxis.set_minor_locator(AutoMinorLocator(minlocs[col]))
ax[(row,col)].axhline(0, lw=0.25, c='0.5')
ax[(0,0)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0))
ax[(0,1)].yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1,decimals=0))
pvvm.plots.despine(ax)
### Legend
patches = [
mpl.patches.Patch(
facecolor=mc[market], edgecolor='none',
label=('Day-ahead' if market == 'da' else 'Real-time'))
for market in ['da','rt']]
leg = ax[(-1,0)].legend(
handles=patches, loc='lower left', frameon=False, ncol=2,
columnspacing=0.5, handletextpad=0.5, handlelength=0.7,)
# plt.tight_layout()
## add big axis, hide frame, ticks, and labels
f.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Difference, revenue-optimized – CF-optimized', weight='bold', y=y2, fontsize='xx-large')
plt.show()
/Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))] /Users/patrickbrown/Dropbox/MITEI/Projects/REValueMap/Package/pvvm/plots.py:490: RuntimeWarning: invalid value encountered in true_divide for i in range(len(binned_data_sets))]